Blinding Yourself

But how to blind myself? I used my pill maker to make 9 OO pills of piracetam mix, and then 9 OO pills of piracetam mix+the Adderall, then I put them in a baggy. The idea is that I can blind myself as to what pill I am taking that day since at the end of the day, I can just look in the baggy and see whether a placebo or Adderall pill is missing: the big capsules are transparent so I can see whether there is a crushed-up blue Adderall in the end or not. If there are fewer Adderall than placebo, I took an Adderall, and vice-versa. Now, since I am checking at the end of each day, I also need to remove or add the opposite pill to maintain the ratio and make it easy to check the next day; more importantly I need to replace or remove a pill, because otherwise the odds will be skewed and I will know how they are skewed. (Imagine I started with 4 Adderalls and 4 placebos, and then 3 days in a row I draw placebos but I don’t add or remove any pills; the next day, because most of the placebos have been used up, there’s only a small chance I will get a placebo…)

This is only one of many ways to blind myself; for example, instead of using one bag, one could use two bags and instead blindly pick a bag to take a pill out of, balancing contents as before. (See also my Vitamin D and day modafinil trials.)

Results

1. Began double-blind trial. Today I took one pill blindly at 1:53 PM. at the end of the day when I have written down my impressions and guess whether it was one of the Adderall pills, then I can look in the baggy and count and see whether it was. there are many other procedures one can take to blind oneself (have an accomplice mix up a sequence of pills and record what the sequence was; don’t count & see but blindly take a photograph of the pill each day, etc.) Around 3, I begin to wonder whether it was Adderall because I am arguing more than usual on IRC and my heart rate seems a bit high just sitting down. 6 PM: I’ve started to think it was a placebo. My heart rate is back to normal, I am having difficulty concentrating on long text, and my appetite has shown up for dinner (although I didn’t have lunch, I don’t think I had lunch yesterday and yesterday the hunger didn’t show up until past 7). Productivity wise, it has been a normal day. All in all, I’m not too sure, but I think I’d guess it was Adderall with 40% confidence (another way of saying ‘placebo with 60% confidence’). When I go to examine the baggie at 8:20 PM, I find out… it was an Adderall pill after all. Oh dear. One little strike against Adderall that I guessed wrong. It may be that the problem is that I am intrinsically a little worse today (normal variation? come down from Adderall?).

   So, a change to the protocol. I will take a pill every other day - a day to washout and reacclimate to ‘baseline’, and then an experimental day. In subsequent entries, assume there was either an at least one intervening break or placebo day.

2. Took random pill at 2:02 PM. Went to lunch half an hour afterwards, talked until 4 - more outgoing than my usual self. I continued to be pretty energetic despite not taking my caffeine+piracetam pills, and though it’s now 12:30 AM and I listened to TAM YouTube videos all day while reading, I feel pretty energetic and am reviewing Mnemosyne cards. I am pretty confident the pill today was Adderall. Hard to believe placebo effect could do this much for this long or that normal variation would account for this. I’d say 90% confidence it was Adderall. I do some more Mnemosyne, typing practice, and reading in a Montaigne book, and finally get tired and go to bed around 1:30 AM or so. I check the baggie when I wake up the next morning, and sure enough, it had been an Adderall pill. That makes me 1 for 2.

3. Took pill 1:27 PM. At 2 my hunger gets the best of me (despite my usual tea drinking and caffeine+piracetam pills) and I eat a large lunch. This makes me suspicious it was placebo - on the previous days I had noted a considerable appetite-suppressant effect. 5:25 PM: I don’t feel unusually tired, but nothing special about my productivity. 8 PM; no longer so sure. Read and excerpted a fair bit of research I had been putting off since the morning. After putting away all the laundry at 10, still feeling active, I check. It was Adderall. I can’t claim this one either way. By 9 or 10 I had begun to wonder whether it was really Adderall, but I didn’t feel confident saying it was; my feeling could be fairly described as 50%.

4. Break; this day/night was for trying armodafinil, pill #1

5. Took pill around 6 PM; I had a very long drive to and from an airport ahead of me, ideal for Adderall. In case it was Adderall, I chewed up the pill - by making it absorb faster, more of the effect would be there when I needed it, during driving, and not lingering in my system past midnight. Was it? I didn’t notice any change in my pulse, I yawned several times on the way back, my conversation was not more voluminous than usual. I did stay up later than usual, but that’s fully explained by walking to get ice cream. All in all, my best guess was that the pill was placebo, and I feel fairly confident but not hugely confident that it was placebo. I’d give it ~70%. And checking the next morning… I was right! Finally.

6. Took pill 12:11 PM. I am not certain. While I do get some things accomplished (a fair amount of work on the Silk Road article and its submission to places), I also have some difficulty reading through a fiction book (Sum) and I seem kind of twitchy and constantly shifting windows. I am weakly inclined to think this is Adderall (say, 60%). It’s not my normal feeling. Next morning - it was Adderall.

7. Week-long break - armodafinil #2 experiment, volunteer work

8. Took pill #6 at 12:35 PM. Hard to be sure. I ultimately decided that it was Adderall because I didn’t have as much trouble as I normally would in focusing on reading and then finishing my novel (Surface Detail) despite my family watching a movie, though I didn’t notice any lack of appetite. Call this one 60-70% Adderall. I check the next evening and it was Adderall.

9. Took pill at 10:50 AM. At 12:30 I watch the new Captain America⁷, and come out as energetic as I went in and was not hungry for snacks at all during it; at this point, I’m pretty confident (70%) that it was Adderall. At 5 I check, and it was. Overall, pretty normal day, save for leading up to the third armodafinil trial.

10. Just 3 Adderall left; took random pill at 12:30. Hopefully I can get a lot of formatting done on hafu. I do manage to do a lot of work on it and my appetite seems minor up until 8 PM, although if not for those two observations; perhaps 60% that it was Adderall. I check the next morning, and it was not.

11. Skipping break day since it was placebo yesterday and I’d like to wind up the Adderall trials. Pill at 12:24 PM. I get very hungry around 3 PM, and it’s an unproductive day even considering how much stress and aggravation and the 3 hours a failed Debian unstable upgrade cost me. I feel quite sure (75%) it was placebo. It was.

12. Took pill at 11:27 AM. Moderately productive. Not entirely sure. 50% either way. (It’s placebo.)

13. Pill at 12:40 PM. I spend entirely too much time arguing matters related to a LW post and on IRC, but I manage to channel it into writing a new mini-essay on my past intellectual sins. This sort of thing seems like Adderall behavior, and I don’t get hungry until much later. All in all, I feel easily 75% sure it’s Adderall; and it was.

14. 12:18 PM. (There are/were just 2 Adderall left now.) I manage to spend almost the entire afternoon single-mindedly concentrating on transcribing two parts of a 1996 Toshio Okada interview (it was very long, and the formatting more challenging than expected), which is strong evidence for Adderall, although I did feel fairly hungry while doing it. I don’t go to bed until midnight and & sleep very poorly - despite taking triple my usual melatonin! Inasmuch as I’m already fairly sure that Adderall damages my sleep, this makes me even more confident (>80%). When I grumpily crawl out of bed and check: it’s Adderall. (One Adderall left.)

15. 10:50 AM. Normal appetite; I try to read through Edward Luttwak’s The Grand Strategy of the Byzantine Empire, slow going. Overall, I guess it was placebo with 70% - I notice nothing I associate with Adderall. I check it at midnight, and it was placebo.

16. 11:30 AM. By 2:30 PM, my hunger is quite strong and I don’t feel especially focused - it’s difficult to get through the tab-explosion of the morning, although one particularly stupid poster on the DNB ML makes me feel irritated like I might on Adderall. I initially figure the probability at perhaps 60% for Adderall, but when I wake up at 2 AM and am completely unable to get back to sleep, eventually racking up a Zeo score of 73 (compared to the usual 100s), there’s no doubt in my mind (95%) that the pill was Adderall. And it was the last Adderall pill indeed.

My predictions were substantially better than random chance⁸, so my default belief - that Adderall does affect me and (mostly) for the better - is borne out. I usually sleep very well and 3 separate incidents of horrible sleep in a few weeks seems rather unlikely (though I didn’t keep track of dates carefully enough to link the Zeo data with the Adderall data). Between the price and the sleep disturbances, I don’t think Adderall is personally worthwhile.

Value of Information (VoI)

See also the discussion as applied to ordering modafinil & evaluating sleep experiments.

The amphetamine mix branded “Adderall” is terribly expensive to obtain even compared to modafinil, due to its tight regulation (a lower schedule than modafinil), popularity in college as a study drug, and reportedly moves by its manufacture to exploit its privileged position as a licensed amphetamine maker to extract more consumer surplus. I paid roughly $4 a pill but could have paid up to $10. Good stimulant hygiene involves recovery periods to avoid one’s body adapting to eliminate the stimulating effects, so even if Adderall was the answer to all my woes, I would not be using it more than 2 or 3 times a week. Assuming 50 uses a year (for specific projects, let’s say, and not ordinary aimless usage), that’s a cool $200 a year. My general belief was that Adderall would be too much of a stimulant for me, as I am amphetamine-naive and Adderall has a bad reputation for letting one waste time on unimportant things. We could say my prediction was 50% that Adderall would be useful and worth investigating further. The experiment was pretty simple: blind randomized pills, 10 placebo & 10 active. I took notes on how productive I was and the next day guessed whether it was placebo or Adderall before breaking the seal and finding out. I didn’t do any formal statistics for it, much less a power calculation, so let’s try to be conservative by penalizing the information quality heavily and assume it had 25%. So $\frac{200-0}{\ln 1.05}\cdot 0.50\cdot 0.25=512$! The experiment probably used up no more than an hour or two total.

Vaniver argues that since I start off not intending to continue Adderall, the analysis actually needs to be different:

In 3, you’re considering adding a new supplement, not stopping a supplement you already use. The “I don’t try Adderall” case has value $0, the “Adderall fails” case is worth −$40 (assuming you only bought 10 pills, and this number should be increased by your analysis time and a weighted cost for potential permanent side effects), and the “Adderall succeeds” case is worth $X-40-4099, where $X is the discounted lifetime value of the increased productivity due to Adderall, minus any discounted long-term side effect costs. If you estimate Adderall will work with p = 0.5, then you should try out Adderall if you estimate that $0.5\cdot (X-4179)>0$ → $X>4179$. (Adderall working or not isn’t binary, and so you might be more comfortable breaking down the various “how effective Adderall is” cases when eliciting X, by coming up with different levels it could work at, their values, and then using a weighted sum to get X. This can also give you a better target with your experiment- “this needs to show a benefit of at least Y from Adderall for it to be worth the cost, and I’ve designed it so it has a reasonable chance of showing that.”)

One thing to notice is that the default case matters a lot. This asymmetry is because you switch decisions in different possible worlds - when you would take Adderall but stop you’re in the world where Adderall doesn’t work, and when you wouldn’t take Adderall but do you’re in the world where Adderall does work (in the perfect information case, at least). One of the ways you can visualize this is that you don’t penalize tests for giving you true negative information, and you reward them for giving you true positive information. (This might be worth a post by itself, and is very Litany of Gendlin.)

Either way, this example demonstrates that anything you are doing expensively is worth testing extensively.

Quasi-Experiment

I noticed what may have been an effect on my dual n-back scores; the difference is not large (▃▆▃▃▂▂▂▂▄▅▂▄▂▃▅▃▄ vs ▃▄▂▂▃▅▂▂▄▁▄▃▅▂▃▂▄▂▁▇▃▂▂▄▄▃▃▂▃▂▂▂▃▄▄▃▆▄▄▂▃▄▃▁▂▂▂▃▂▄▂▁▁▂▄▁▃▂▄) and appears mostly in the averages - Toomim’s quick two-sample t-test gave p = 0.23, although an another analysis gives p = 0.1381¹⁴. One issue with this before-after quasi-experiment is that one would expect my scores to slowly rise over time and hence a fish oil after would yield a score increase - the 3.2 point difference could be attributable to that, placebo effect, or random variation etc. But an accidentally noticed effect (d = 0.28) is a promising start. An experiment may be worth doing given that fish oil does cost a fair bit each year: randomized blocks permitting an fish-oil-then-placebo comparison would take care of the first issue, and then blinding (olive oil capsules versus fish oil capsules?) would take care of the placebo worry.

Power Calculation

We have clear hypotheses here, so we can be a little optimistic: the fish oil will either improve mood or scores or it will do nothing; it will not worsen either. First, the large anxiety effect:

pwr.t.test(d=3.55,type="paired",power=0.75,alternative="greater",sig.level=0.05)
#      Paired t test power calculation
#
#               n = 2.269155
#
#               NOTE: n is number of *pairs*

Suspiciously easy. 2.25 pairs or 6 blocks? Let’s be pessimistic and use the smaller effect size estimate from my quasi-trial:

# pwr.t.test(d=0.28,type="paired",power=0.75,alternative="greater",sig.level=0.05)
#
#      Paired t test power calculation
#
#               n = 69.98612

70 pairs is 140 blocks; we can drop to 36 pairs or 72 blocks if we accept a power of 0.5/50% chance of reaching significance. (Or we could economize by hoping that the effect size is not 3.5 but maybe twice the pessimistic guess; a d = 0.5 at 50% power requires only 12 pairs of 24 blocks.) 70 pairs of blocks of 2 weeks, with 2 pills a day requires $(70\cdot 2)\cdot (2\cdot 7)\cdot 2=3920$ pills. I don’t even have that many empty pills! I have <500; 500 would supply 250 days, which would yield 18 2-week blocks which could give 9 pairs. 9 pairs would give me a power of:

pwr.t.test(d=0.28,type="paired",alternative="greater",sig.level=0.05,n=9)
# ...          power = 0.1908962
pwr.t.test(d=0.5,type="paired",alternative="greater",sig.level=0.05,n=9)
# ...          power = 0.3927739

A 20-40% chance of detecting the effect.

VoI

For background on “value of information” calculations, see the Adderall calculation.

1. Cost of fish oil:

   The price is not as good as multivitamins or melatonin. The studies showing effects generally use pretty high dosages, 1-4g daily. I took 4 capsules a day for roughly 4g of omega acids. The jar of 400 is 100 days’ worth, and costs ~$17, or around 17¢ a day. The general health benefits push me over the edge of favoring its indefinite use, but looking to economize. Usually, small amounts of packaged substances are more expensive than bulk unprocessed, so I looked at fish oil fluid products; and unsurprisingly, liquid is more cost-effective than pills (but like with the powders, straight fish oil isn’t very appetizing) in lieu of membership somewhere or some other price-break. I bought 4 bottles (16 fluid ounces each) for $53.31 total (thanks to coupons & sales), and each bottle lasts around a month and a half for perhaps half a year, or ~$100 for a year’s supply. (As it turned out, the 4 bottles lasted from 2010-12-04 to 2011-06-17, or 195 days.) My next batch lasted 2011-08–19-2012-02-20, and cost $58.27. Since I needed to buy empty 00 capsules (for my lithium experiment) and a book (Stanovich2010, for SIAI work) from Amazon, I bought 4 more bottles of 16fl oz Nature’s Answer (lemon-lime) at $48.44, which I began using 2012-02-27. So call it ~$70 a year.

   Most of the most solid fish oil results seem to meliorate the effects of age; in my 20s, I’m not sure they are worth the cost. But I would probably resume fish oil in my 30s or 40s when aging really becomes a concern. So the experiment at most will result in discontinuing for a decade. At $X a year, that’s a net present value of sum $ map (\n -> 70 / (1 + 0.05)^n) [1..10] = $540.5.

2. Cost of experimentation:

   The fish oil can be considered a free sunk cost: I would take it in the absence of an experiment. The empty pill capsules could be used for something else, so we’ll put the 500 at $5. Filling 500 capsules with fish and olive oil will be messy and take an hour. Taking them regularly can be added to my habitual morning routine for vitamin D and the lithium experiment, so that is close to free but we’ll call it an hour over the 250 days. Recording mood/productivity is also free a sunk cost as it’s necessary for the other experiments; but recording dual n-back scores is more expensive: each round is ~2 minutes and one wants >=5, so each block will cost >10 minutes, so 18 tests will be >180 minutes or >3 hours. So >5 hours. Total: $5+(>5\cdot 7.25)=>41$.

3. Priors:

   The power calculation indicates a 20% chance of getting useful information. My quasi-experiment has <70% chance of being right, and I preserve a general skepticism about any experiment, even one as well done as the medical student one seems to be, and give that one a <80% chance of being right; so let’s call it 70% the effect exists, or 30% it doesn’t exist (which is the case in which I save money by dropping fish oil for 10 years).

4. Value of Information

   Power times prior times benefit minus cost of experimentation: $(0.20\cdot 0.30\cdot 540)-41=-9$. So the VoI is negative: because my default is that fish oil works and I am taking it, weak information that it doesn’t work isn’t enough. If the power calculation were giving us 40% reliable information, then the chance of learning I should drop fish oil is improved enough to make the experiment worthwhile (going from 20% to 40% switches the value from −$9 to +$23.8).

Design

Most of the reported benefits of lithium are impossible for me to test: rates of suicide and Parkinson’s are right out, but so is crime and neurogenesis (the former is too rare & unusual, the latter too subtle & hard to measure), and likewise potential negatives. So we could measure:

1. mood, via daily self-report; should increase

   The principal metric would be ‘mood’, however defined. Zeo’s web interface & data export includes a field for ‘Day Feel’, which is a rating 1-5 of general mood & quality of day. I can record a similar metric at the end of each day. 1-5 might be a little crude even with a year of data, so a more sophisticated measure might be in order. The first mood study is paywalled so I’m not sure what they used, but Shiotsuki 2008_16ya used State-Trait of Anxiety Inventory (STAI) and Profiles of Mood States Test (POMS). The full POMS sounds too long to use daily, but the Brief POMS might work. In the original 1987_37ya paper “A brief POMS measure of distress for cancer patients”, patients answering this questionnaire had a mean total mean of 10.43 (standard deviation 8.87). Is this the best way to measure mood? I’ve asked Seth Roberts; he suggested using a 0-100 scale, but personally, there’s no way I can assess my mood on 0-100. My mood is sufficiently stable (to me) that 0-5 is asking a bit much, even.

   I ultimately decided to just go with the simple 0-5 scale, although it seems to have turned out to be more of a 2-4 scale! Apparently I’m not very good at introspection.

2. long-term memory (Mnemosyne 2.0’s statistics); could increase (neurogenesis), do nothing (null result), or decrease (metal poisoning)

3. working memory (dual n-back scores via Brain Workshop¹⁵); like long-term memory

4. sleep (Zeo); should increase (via mood improvement)

5. time procrastinating on computer (arbtt daemon every 10-40 seconds records open & active windows; these statistics can be parsed into categories like work or play. Total time on latter categories could be a useful metric. A second metric would be number of commits to the Gwern.net source repository.)

Lithium is somewhat persistent in the body, and its effects are not acute especially in low doses; this calls for long blocked trials.

The blood half-life is 12-36 hours; hence two or three days ought to be enough to build up and wash out. A week-long block is reasonable since that gives 5 days for effects to manifest, although month-long blocks would not be a bad choice either. (I prefer blocks which fit in round periods because it makes self-experiments easier to run if the blocks fit in normal time-cycles like day/week/month. The most useless self-experiment is the one abandoned halfway.)

With subtle effects, we need a lot of data, so we want at least half a year (6 blocks) or better yet, a year (12 blocks); this requires 180 actives and 180 placebos. This is easily covered by $11 for “Doctor’s Best Best Lithium Orotate (5mg), 200-Count” (more precisely, “Lithium 5mg (from 125mg of lithium orotate)”) and $14 for 1000_1,024yax1g empty capsules (purchased February 2012_12ya). For convenience I settled on 168 lithium & 168 placebos (7 pill-machine batches, 14 batches total); I can use them in 24 paired blocks of 7-days/1-week each (48 total blocks/48 weeks). The lithium expiration date is October 2014, so that is not a problem

The methodology would be essentially the same as the vitamin D in the morning experiment: put a multiple of 7 placebos in one container, the same number of actives in another identical container, hide & randomly pick one of them, use container for 7 days then the other for 7 days, look inside them for the label to determine which period was active and which was placebo, refill them, and start again.

VoI

For background on “value of information” calculations, see the Adderall calculation.

Low-dose lithium orotate is extremely cheap, ~$10 a year. There is some research literature on it improving mood and impulse control in regular people, but some of it is epidemiological (which implies considerable unreliability); my current belief is that there is probably some effect size, but at just 5mg, it may be too tiny to matter. I have ~40% belief that there will be a large effect size, but I’m doing a long experiment and I should be able to detect a large effect size with >75% chance. So, the formula is NPV of the difference between taking and not taking, times quality of information, times expectation: $\frac{10-0}{\ln 1.05}\cdot 0.75\cdot 0.40=61.4$, which justifies a time investment of less than 9 hours. As it happens, it took less than an hour to make the pills & placebos, and taking them is a matter of seconds per week, so the analysis will be the time-consuming part. This one may actually turn a profit.

Data

1. first pair

   1. first block started and pill taken: 2012-05-11 - 19 May: 1

   2. 20 May - 27: 0

2. second pair

   1. first block started and pill taken: 29 May - 4 June: 1

   2. second block: 5 June - 11 June: 0

3. third pair

   1. first block: 12 June - 18 June: 1

   2. second block: 19 June - 25 June: 0

4. fourth pair

   1. first block: 26 June - 2 July: 1

   2. second block: 3 July - 8 July: 0

5. fifth pair

   1. first block: 13 July - 20 July: 1

   2. second block: 21 July - 27 July: 0

6. sixth pair

   1. first block: 28 July - 3 August: 0

   2. second block: 4 August - 10 August: 1

7. seventh pair

   1. first block: 11 August - 17 August: 1

   2. second block: 18 August - 24 August: 0

8. eighth pair

   1. first block: 25 August - 31 August: 1

   2. second block: 1 September - 4 September, stopped until 24 September, finished 25 September: 0

9. I interrupted the lithium self-experiment until March 2013_11ya in order to run the LSD microdosing self-experiment without a potential confound; ninth block pair:

   1. 2013-03-12 - 18 March: 1

   2. 19 March - 25 March: 0

10. tenth pair:

    1. 26 March - 1 April: 0

    2. 2 April - 8 April: 1

11. eleventh pair:

    1. 9 April - 15 April: 0

    2. 16 April - 21 April: 1

12. twelfth pair:

    1. 22 April - 28 April: 1

    2. 29 April - 5 May: 0

13. thirteenth pair:

    1. 6 May - 12 May: 0

    2. 13 May - 19 May: 1

14. fourteenth pair:

    1. 20 May - 26 May: 1

    2. 27 May - 2 June: 0

15. fifteenth:

    1. 5 June - 11 June: 0

    2. 12 June - 18 June: 1

16. sixteenth:

    1. 19 June - 25 June: 0

    2. 26 June - 2 July: 1

17. seventeenth:

    1. 3 July - 9 July: 0

    2. 10 July - 16 July: 1

18. eighteenth:

    1. 17 July - 23 July: 0

    2. 24 July - 28 July, 8 August - 9 August: 1

19. nineteenth:

    1. 10 August - 16 August: 0

    2. 17 August - 23 August: 1

20. twentieth:

    1. 24 August - 30 August: 0

    2. 3 September - 6 September: 1

21. twenty-first:

    1. 7 September - 13 September: 1

    2. 14 September - 20 September: 0

22. twenty-second:

    1. 21 September - 27 September: 0

    2. 28 September - 4 October: 1

23. twenty-third:

    1. 5 October - 11 October: 0

    2. 12 October - 18 October: 1

24. twenty-fourth:

    1. 20 - 26 October: 0

    2. 27 October - 2 November: 1

Analysis

lithium <- read.csv("lithium.csv")
lithium$Date <- as.Date(lithium$Date)
rm(lithium$X)

mp <- read.csv("mp.csv")
mp$Date <- as.Date(mp$Date)

mnemosyne <- read.csv("gwern-mnemosyne.csv", header=FALSE,
                      col.names =c("Timestamp", "Easiness", "Grade"),
                      colClasses=c("integer",   "numeric",  "integer"))
mnemosyne$Date <- as.Date(as.POSIXct(mnemosyne$Timestamp, origin = "1970-01-01", tz = "EST"))
mnemosyne <- mnemosyne[mnemosyne$Date>as.Date("2012-05-11") & mnemosyne$Date<as.Date("2013-11-02"),]
mnemosyne <- aggregate(mnemosyne$Grade, by=list(mnemosyne$Date), FUN=function (x) { mean(as.vector(x));})
colnames(mnemosyne) <- c("Date", "Mnemosyne.grade")

zeo <- read.csv("https://gwern.net/doc/zeo/gwern-zeodata.csv")
zeo$Sleep.Date <- as.Date(zeo$Sleep.Date, format="%m/%d/%Y")
colnames(zeo)[1]  <- "Date"
zeo <- zeo[zeo$Date>as.Date("2012-05-11") & zeo$Date<as.Date("2013-11-02"),]
zeo <- zeo[,c(1:10, 23)]

zeo$Start.of.Night <- sapply(strsplit(as.character(zeo$Start.of.Night), " "), function(x) { x[[2]] })
## convert "06:45" to 24300
interval <- function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                           else { y <- unlist(strsplit(x, ":"));
                                                  as.integer(y[[1]])*60 + as.integer(y[[2]]); }
                                                  }
                          else NA
                          }
zeo$Start.of.Night <- sapply(zeo$Start.of.Night, interval)
## the night 'wraps around' at ~800, so let's take 0-400 and add +800 to reconstruct 'late at night'
zeo[zeo$Start.of.Night<400,]$Start.of.Night <- (zeo[zeo$Start.of.Night<400,]$Start.of.Night + 800)

arbtt1 <- read.csv("20122013-arbtt.csv")
arbtt2 <- read.csv("20132014-arbtt.csv")
arbtt <- rbind(arbtt1, arbtt2)
arbtt <- arbtt[as.Date(arbtt$Day)>=as.Date("2012-05-11") & as.Date(arbtt$Day)<=as.Date("2013-11-02"),]
## rename Day -> Date, delete Percentage
arbtt <- with(arbtt, data.frame(Date=Day, Tag=Tag, Time=Time))
## Convert time-lengths to second-counts: "0:16:40" to 1000 (seconds); "7:57:30" to 28650 (seconds) etc.
## We prefer units of seconds since arbtt has sub-minute resolution and not all categories
## will have a lot of time each day.
interval <- function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                           else { y <- unlist(strsplit(x, ":"));
                                                  as.integer(y[[1]])*3600 +
                                                   as.integer(y[[2]])*60 +
                                                   as.integer(y[[3]]);
                                                 }
                                          }
                          else NA
                          }
arbtt$Time <- sapply(as.character(arbtt$Time), interval)
library(reshape)
arbtt <- reshape(arbtt, v.names="Time", timevar="Tag", idvar="Date", direction="wide")
arbtt[is.na(arbtt)] <- 0
arbtt$Date <- as.Date(arbtt$Date)

patches <- read.csv("patchlog.txt")
patches$Date <- as.Date(patches$Date)

## merge all the previous data into a single data-frame:
lithiumExperiment <- merge(merge(merge(merge(merge(lithium, mp), mnemosyne, all=TRUE),
                            patches, all=TRUE), arbtt, all=TRUE), zeo, all=TRUE)
## no patches recorded for a day == 0 patches that day
lithiumExperiment[is.na(lithiumExperiment$Gwern.net.patches),]$Gwern.net.patches  <- 0
## NA=I didn't do SRS that day; but that is bad and should be penalized!
lithiumExperiment[is.na(lithiumExperiment$Mnemosyne.grade),]$Mnemosyne.grade  <- 0

productivity <- lithiumExperiment[,c(3,5:22)]
library(psych) ## for factor analysis
nfactors(productivity)
# VSS complexity 1 achieves a maximum of 0.58  with  14  factors
# VSS complexity 2 achieves a maximum of 0.67  with  14  factors
# The Velicer MAP achieves a minimum of 0.02  with  1  factors
# Empirical BIC achieves a minimum of  -304.3  with  4  factors
# Sample Size adjusted BIC achieves a minimum of  -97.84  with  7  factors
#
# Statistics by number of factors
#    vss1 vss2   map dof   chisq     prob sqresid  fit RMSEA    BIC SABIC complex  eChisq    eRMS
# 1  0.16 0.00 0.016 152 1.3e+03 2.6e-190    20.4 0.16 0.122  389.4 871.9     1.0 2.1e+03 1.1e-01
# 2  0.27 0.31 0.022 134 7.8e+02  1.9e-91    16.7 0.31 0.095  -65.2 360.1     1.3 1.1e+03 7.9e-02
# 3  0.30 0.40 0.021 117 4.9e+02  5.2e-47    14.3 0.41 0.078 -247.2 124.2     1.6 7.0e+02 6.2e-02
# 4  0.39 0.47 0.024 101 2.5e+02  4.1e-14    12.1 0.50 0.052 -389.8 -69.2     1.7 3.4e+02 4.3e-02
# 5  0.39 0.51 0.028  86 1.9e+02  2.5e-10    11.2 0.54 0.049 -347.4 -74.4     1.7 2.4e+02 3.6e-02
# 6  0.41 0.53 0.034  72 1.4e+02  7.9e-06    10.3 0.57 0.041 -317.3 -88.8     1.6 1.7e+02 3.1e-02
# 7  0.44 0.54 0.041  59 8.6e+01  1.2e-02     9.6 0.60 0.030 -285.1 -97.8     1.8 1.1e+02 2.5e-02
# 8  0.40 0.52 0.050  47 1.1e+02  1.4e-07     9.9 0.59 0.053 -181.2 -32.0     2.0 2.0e+02 3.3e-02
# 9  0.48 0.57 0.063  36 4.6e+01  1.1e-01     8.3 0.66 0.024 -180.2 -65.9     1.7 6.0e+01 1.8e-02
# 10 0.51 0.62 0.079  26 1.9e+01  8.3e-01     7.2 0.70 0.000 -144.6 -62.1     1.6 1.9e+01 1.0e-02
# 11 0.52 0.62 0.098  17 1.4e+01  6.8e-01     6.7 0.72 0.000  -93.2 -39.3     1.7 1.5e+01 9.0e-03
# 12 0.52 0.61 0.124   9 1.1e+01  3.1e-01     6.7 0.72 0.020  -46.1 -17.5     1.6 1.3e+01 8.3e-03
# 13 0.48 0.61 0.163   2 4.9e+00  8.6e-02     6.3 0.74 0.053   -7.7  -1.3     1.8 6.2e+00 5.8e-03
# 14 0.58 0.67 0.210  -4 7.5e-03       NA     4.9 0.80    NA     NA    NA     1.8 9.0e-03 2.2e-04
# 15 0.56 0.64 0.293  -9 4.6e-06       NA     5.3 0.78    NA     NA    NA     2.0 6.1e-06 5.7e-06
# 16 0.53 0.62 0.465 -13 8.7e-07       NA     5.5 0.77    NA     NA    NA     2.1 8.6e-07 2.2e-06
# 17 0.51 0.61 0.540 -16 9.3e-12       NA     5.6 0.77    NA     NA    NA     2.1 1.1e-11 7.8e-09
# 18 0.51 0.61 1.000 -18 7.0e-10       NA     5.6 0.77    NA     NA    NA     2.1 7.8e-10 6.5e-08
# 19 0.51 0.61    NA -19 0.0e+00       NA     5.6 0.77    NA     NA    NA     2.1 6.2e-25 1.8e-15
#    eCRMS   eBIC
# 1  0.112 1107.9
# 2  0.089  303.3
# 3  0.075  -31.6
# 4  0.055 -300.5
# 5  0.050 -304.3
# 6  0.047 -280.3
# 7  0.042 -257.4
# 8  0.062  -97.2
# 9  0.039 -167.1
# 10 0.026 -144.7
# 11 0.028  -92.1
# 12 0.036  -44.0
# 13 0.054   -6.4
# 14    NA     NA
# 15    NA     NA
# 16    NA     NA
# 17    NA     NA
# 18    NA     NA
# 19    NA     NA

factorization <- fa(productivity, nfactors=4); factorization
# Standardized loadings (pattern matrix) based upon correlation matrix
#                     MR3   MR1   MR2   MR4     h2    u2 com
# MP                 0.05  0.01 -0.02  0.34 0.1241 0.876 1.1
# Gwern.net.patches -0.04  0.01  0.01  0.48 0.2241 0.776 1.0
# Time.WWW           0.98 -0.04 -0.10  0.02 0.9778 0.022 1.0
# Time.X             0.49  0.29  0.47 -0.03 0.5801 0.420 2.6
# Time.IRC           0.35 -0.06 -0.14  0.16 0.1918 0.808 1.8
# Time.Writing       0.04 -0.01  0.04  0.69 0.4752 0.525 1.0
# Time.Stats         0.42 -0.10  0.30  0.01 0.2504 0.750 1.9
# Time.PDF          -0.09 -0.05  0.98  0.00 0.9791 0.021 1.0
# Time.Music         0.10 -0.10  0.02  0.03 0.0196 0.980 2.2
# Time.Rec           0.03  0.99 -0.03 -0.02 0.9950 0.005 1.0
# Time.SRS           0.06 -0.06  0.07  0.10 0.0209 0.979 3.4
# Time.Sysadmin      0.22  0.13 -0.04  0.13 0.0953 0.905 2.4
# Time.DNB          -0.04 -0.05 -0.06  0.07 0.0149 0.985 3.3
# Time.Bitcoin       0.15 -0.07 -0.07 -0.04 0.0306 0.969 2.1
# Time.Blackmarkets  0.18 -0.09 -0.08  0.02 0.0470 0.953 1.9
# Time.Programming  -0.04  0.05 -0.04  0.43 0.1850 0.815 1.1
# Time.Backups      -0.09  0.06 -0.01  0.04 0.0114 0.989 2.4
# Time.Umineko      -0.16  0.71 -0.03  0.06 0.5000 0.500 1.1
# Time.Typing       -0.03 -0.04  0.02 -0.01 0.0034 0.997 2.4
#
#                        MR3  MR1  MR2  MR4
# SS loadings           1.67 1.64 1.33 1.08
# Proportion Var        0.09 0.09 0.07 0.06
# Cumulative Var        0.09 0.17 0.24 0.30
# Proportion Explained  0.29 0.29 0.23 0.19
# Cumulative Proportion 0.29 0.58 0.81 1.00
#
#  With factor correlations of
#       MR3   MR1   MR2   MR4
# MR3  1.00  0.12 -0.05  0.10
# MR1  0.12  1.00  0.07 -0.08
# MR2 -0.05  0.07  1.00 -0.08
# MR4  0.10 -0.08 -0.08  1.00
#
# Mean item complexity =  1.8
# Test of the hypothesis that 4 factors are sufficient.
#
# The degrees of freedom for the null model are 171
# and the objective function was 3.08 with Chi Square of 1645
# The degrees of freedom for the model are 101  and the objective function was  0.46
#
# The root mean square of the residuals (RMSR) is  0.04
# The df corrected root mean square of the residuals is  0.06
#
# The harmonic number of observations is  538 with the empirical chi square  332.7  with prob <  1.6e-26
# The total number of observations was  542  with MLE Chi Square =  246  with prob <  4.1e-14
#
# Tucker Lewis Index of factoring reliability =  0.832
# RMSEA index =  0.052  and the 90 % confidence intervals are  0.043 0.06
# BIC =  -389.8
# Fit based upon off diagonal values = 0.88
# Measures of factor score adequacy
#                                                 MR3  MR1  MR2  MR4
# Correlation of scores with factors             0.99 1.00 0.99 0.79
# Multiple R square of scores with factors       0.98 0.99 0.98 0.63
# Minimum correlation of possible factor scores  0.95 0.99 0.96 0.25

## I interpret MR3=Internet+Stats usage; MR1=goofing off; MR2=reading/stats; MR4=writing
## I don't care about MR1, so we'll look for effects on 3/2/4:
lithiumExperiment$MR3 <- predict(factorization, data=productivity)[,1]
lithiumExperiment$MR2 <- predict(factorization, data=productivity)[,3]
lithiumExperiment$MR4 <- predict(factorization, data=productivity)[,4]
write.csv(lithiumExperiment, file="2012-lithium-experiment.csv", row.names=FALSE)

lithiumExperiment <- read.csv("https://gwern.net/doc/psychiatry/lithium/2012-lithium-experiment.csv")
l1 <- lm(cbind(MP, Mnemosyne.grade, Gwern.net.patches, ZQ, MR3, MR2, MR4) ~ Lithium, data=lithiumExperiment)
summary(l1)
# Response MP :
#
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   3.0613     0.0591    51.8   <2e-16
# Lithium      -0.0425     0.0841    -0.5     0.61
#
# Residual standard error: 0.755 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  0.000796,    Adjusted R-squared:  -0.00233
# F-statistic: 0.255 on 1 and 320 DF,  p-value: 0.614
#
#
# Response Mnemosyne.grade :
#
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)    3.158      0.120   26.41   <2e-16
# Lithium       -0.141      0.170   -0.83     0.41
#
# Residual standard error: 1.53 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  0.00214, Adjusted R-squared:  -0.000975
# F-statistic: 0.687 on 1 and 320 DF,  p-value: 0.408
#
#
# Response Gwern.net.patches :
#
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   3.8712     0.3271   11.83   <2e-16
# Lithium       0.0345     0.4655    0.07     0.94
#
# Residual standard error: 4.18 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  1.72e-05,    Adjusted R-squared:  -0.00311
# F-statistic: 0.00549 on 1 and 320 DF,  p-value: 0.941
#
#
# Response ZQ :
#
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   91.773      1.024   89.66   <2e-16
# Lithium        0.523      1.457    0.36     0.72
#
# Residual standard error: 13.1 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  0.000402,    Adjusted R-squared:  -0.00272
# F-statistic: 0.129 on 1 and 320 DF,  p-value: 0.72
#
#
# Response MR3 :
#
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)  -0.0258     0.0691   -0.37     0.71
# Lithium       0.0657     0.0983    0.67     0.50
#
# Residual standard error: 0.882 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  0.00139, Adjusted R-squared:  -0.00173
# F-statistic: 0.447 on 1 and 320 DF,  p-value: 0.504
#
#
# Response MR2 :
#
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   0.0187     0.0788    0.24     0.81
# Lithium       0.0435     0.1121    0.39     0.70
#
# Residual standard error: 1.01 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  0.00047, Adjusted R-squared:  -0.00265
# F-statistic: 0.15 on 1 and 320 DF,  p-value: 0.698
#
#
# Response MR4 :
#
# Coefficients:
#              Estimate Std. Error t value Pr(>|t|)
# (Intercept)  0.000209   0.052772    0.00     1.00
# Lithium     -0.073464   0.075099   -0.98     0.33
#
# Residual standard error: 0.674 on 320 degrees of freedom
#   (220 observations deleted due to missingness)
# Multiple R-squared:  0.00298, Adjusted R-squared:  -0.000134
# F-statistic: 0.957 on 1 and 320 DF,  p-value: 0.329

summary(manova(l1))
#            Df      Pillai  approx F num Df den Df  Pr(>F)
# Lithium     1 0.009477169 0.4291862      7    314 0.88373
# Residuals 320

Conclusion

There were no observable effects, either positive or beneficial, to the lithium orotate doses. This is consistent with my subjective experience. So I will not be using lithium orotate anymore.

LLLT Pilot Analysis

The correlation of LLLT usage with higher MP self-rating is fairly large (r = 0.19 / d = 0.455) and statistically-significant (p = 0.0006).

I have no particularly compelling story for why this might be a correlation and not causation. It could be placebo, but I wasn’t expecting that. It could be selection effect (days on which I bothered to use the annoying LED set are better days) but then I’d expect the off-days to be below-average and compared to the 2 years of trendline before, there doesn’t seem like much of a fall.

The R code:

lllt <- read.csv("https://gwern.net/doc/nootropic/quantified-self/2014-08-03-lllt-correlation.csv")
l <- lm(MP ~ LLLT + as.logical(Magnesium.citrate) + as.integer(Date) +
              as.logical(Magnesium.citrate):as.integer(Date),
           data=lllt); summary(l)
# ...Coefficients:
#                                                        Estimate   Std. Error  t value   Pr(>|t|)
# (Intercept)                                         4.037702597  0.616058589  6.55409 5.0282e-10
# LLLTTRUE                                            0.330923350  0.095939634  3.44929 0.00069087
# as.logical(Magnesium.citrate)TRUE                   0.963379487  0.842463568  1.14353 0.25424378
# as.integer(Date)                                   -0.001269089  0.000880949 -1.44059 0.15132856
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.001765953  0.001213804 -1.45489 0.14733212

0.330923350 / sd(lllt$MP, na.rm=TRUE)
# [1] 0.455278787

cor.test(lllt$MP, as.integer(lllt$LLLT))
#
#   Pearson`s product-moment correlation
#
# data:  lllt$MP and as.integer(lllt$LLLT)
# t = 3.4043, df = 327, p-value = 0.0007458
# alternative hypothesis: true correlation is not equal to 0
# 95% confidence interval:
#  0.0784517682 0.2873891665
# sample estimates:
#         cor
# 0.185010342

## check whether there's odd about non-LLLT days by expanding to include baseline
llltImputed <- lllt
llltImputed[is.na(llltImputed)] <- 0
llltImputed[llltImputed$MP == 0,]$MP <- 3 # clean up an outlier using median

summary(lm(MP ~ LLLT + as.logical(Magnesium.citrate) + as.integer(Date) +
                as.logical(Magnesium.citrate):as.integer(Date),
           data=llltImputed))
# ...Coefficients:
#                                                        Estimate   Std. Error  t value   Pr(>|t|)
# (Intercept)                                         2.959172295  0.049016571 60.37085 < 2.22e-16
# LLLT                                                0.336886970  0.083731179  4.02344 6.2212e-05
# as.logical(Magnesium.citrate)TRUE                   2.155586397  0.619675529  3.47857 0.00052845
# as.integer(Date)                                    0.000181441  0.000103582  1.75166 0.08017565
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.003373682  0.000904342 -3.73054 0.00020314

power.t.test(power=0.8,
             delta=(0.336886970 / sd(lllt$MP, na.rm=TRUE)),
             type="paired",
             alternative="one.sided")
#
#      Paired t test power calculation
#
#               n = 30.1804294
#           delta = 0.463483435
#              sd = 1
#       sig.level = 0.05
#           power = 0.8
#     alternative = one.sided
#
# NOTE: n is number of *pairs*, sd is std.dev. of *differences* within pairs

library(ggplot2)
llltImputed$Date <- as.Date(llltImputed$Date)
ggplot(data = llltImputed, aes(x=Date, y=MP, col=as.logical(llltImputed$LLLT))) +
 geom_point(size=I(3)) +
 stat_smooth() +
 scale_colour_manual(values=c("gray49", "green"),
                     name = "LLLT")

So, I have started a randomized experiment; should take 2 months, given the size of the correlation. If that turns out to be successful too, I’ll have to look into methods of blinding - for example, some sort of electronic doohickey which turns on randomly half the time and which records whether it’s on somewhere one can’t see. (Then for the experiment, one hooks up the LED, turns the doohickey ‘on’, and applies directly to forehead, checking the next morning to see whether it was really on or off).

lllt <- read.csv("https://gwern.net/doc/nootropic/quantified-self/2014-08-03-lllt-correlation.csv")
zeo <- read.csv("https://gwern.net/doc/zeo/gwern-zeodata.csv")
lllt$Date <- as.Date(lllt$Date)
zeo$Date <- as.Date(zeo$Sleep.Date, format="%m/%d/%Y")

sleepLLLT <- merge(lllt, zeo, all=TRUE)
l <- lm(cbind(Start.of.Night, Time.to.Z, Time.in.Wake, Awakenings, Time.in.REM, Time.in.Light, Time.in.Deep, Total.Z, ZQ, Morning.Feel) ~ LLLT, data=sleepLLLT)
summary(manova(l))
##            Df     Pillai approx F num Df den Df  Pr(>F)
## LLLT        1 0.04853568 1.617066     10    317 0.10051
## Residuals 326
library(ggplot2)
qplot( sleepLLLT$Date, sleepLLLT$Total.Z, color=sleepLLLT$LLLT)

lllt <- read.csv("~/wiki/doc/nootropic/quantified-self/2014-08-03-lllt-correlation.csv",
                 colClasses=c("Date",rep("integer", 4), "logical"))
lllt <- data.frame(Date=lllt$Date, LLLT=lllt$LLLT)
mp <- read.csv("~/selfexperiment/mp.csv", colClasses=c("Date", "integer"))
creativity <- read.csv("~/selfexperiment/dailytodo-marchjunecreativity.csv",
                       colClasses=c("Date", "integer"))
mnemosyne <- read.csv("~/selfexperiment/mnemosyne.csv", header=FALSE,
                      col.names =c("Timestamp", "Easiness", "Grade"),
                      colClasses=c("integer",   "numeric",  "integer"))
mnemosyne$Timestamp <- as.POSIXct(mnemosyne$Timestamp, origin = "1970-01-01", tz = "EST")
mnemosyne$Date    <- as.Date(mnemosyne$Timestamp)
mnemosyne <- aggregate(Grade ~ Date, mnemosyne, mean)
mnemosyne$Average.Spaced.repetition.score <- mnemosyne$Grade
rm(mnemosyne$Grade)

dnb <- read.csv("~/doc/brainworkshop/data/stats.txt", header=FALSE)
dnb$V1 <- as.POSIXct(dnb$V1, format="%F %R:%S")
dnb <- dnb[!is.na(dnb$V1),]
dnb <- with(dnb, data.frame(Timestamp=V1, Nback.type=V2, Percentage=V3))
dnb$Date <- as.Date(dnb$Timestamp)
dnbDaily <- aggregate(Percentage ~ Date + Nback.type, dnb, mean)

arbtt1 <- read.csv("~/selfexperiment/2012-2013-arbtt.txt")
arbtt2 <- read.csv("~/selfexperiment/2013-2014-arbtt.txt")
arbtt <- rbind(arbtt1, arbtt2)
rm(arbtt$Percentage)
interval <- function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                          else { y <- unlist(strsplit(x, ":"));
                                                 as.integer(y[[1]])*3600 +
                                                  as.integer(y[[2]])*60 +
                                                  as.integer(y[[3]]);
                                                }
                                          }
                         else NA
                         }
arbtt$Time <- sapply(as.character(arbtt$Time), interval)
library(reshape)
arbtt <- reshape(arbtt, v.names="Time", timevar="Tag", idvar="Day", direction="wide")
arbtt$Date <- as.Date(arbtt$Day)
rm(arbtt$Day)
arbtt[is.na(arbtt)] <- 0

patches <- read.csv("~/selfexperiment/patchlog-gwern.net.txt", colClasses=c("integer", "Date"))
patches$Gwern.net.patches.log <- log1p(patches$Gwern.net.patches)

# modified lines per day is much harder: state machine to sum lines until it hits the next date
patchCount <- scan(file="~/selfexperiment/patchlog-linecount-gwern.net.txt", character(), sep = "\n")
patchLines <- new.env()
for (i in 1:length(patchCount)) {
 if (grepl("\t", patchCount[i])) { patchLines[[date]] <- patchLines[[date]] +
                                                          sum(sapply(strsplit(patchCount[i], "\t"), as.integer))
                                  }
  else { date <- patchCount[i]
   patchLines[[date]] <- 0 }
   }
patchLines <- as.list(patchLines)
patchLines <- data.frame(
         Date = rep(names(patchLines), lapply(patchLines, length)),
         Gwern.net.linecount= unlist(patchLines))
rm(row.names(patchLines))
patchLines$Date <- as.Date(patchLines$Date)
patchLines$Gwern.net.linecount.log <- log1p(patchLines$Gwern.net.linecount)

firstDay <- patches$Date[1]; lastDay <- patches$Date[nrow(patches)]
patches <- merge(merge(patchLines, patches, all=TRUE), data.frame(Date=seq(firstDay, lastDay, by="day")), all=TRUE)

# if entries are missing, they == 0
patches[is.na(patches)] <- 0

# combine all the data:
llltData <- merge(merge(merge(merge(merge(lllt, mp, all=TRUE), creativity, all=TRUE), dnbDaily, all=TRUE), arbtt, all=TRUE), patches, all=TRUE)
write.csv(llltData, file="2014-08-08-lllt-correlation-factoranalysis.csv", row.names=FALSE)

lllt <- read.csv("https://gwern.net/doc/nootropic/quantified-self/2014-08-08-lllt-correlation-factoranalysis.csv")
## the log transforms are more useful:
rm(lllt$Date, lllt$Nback.type, lllt$Gwern.net.linecount, lllt$Gwern.net.patches)

## https://stats.stackexchange.com/questions/28576/filling-nas-in-a-dataset-with-column-medians-in-r
imputeColumnAsMedian <- function(x){
   x[is.na(x)] <- median(x, na.rm=TRUE) #convert the item with NA to median value from the column
   x #display the column
}
llltI  <- data.frame(apply(lllt, 2, imputeColumnAsMedian))

library(psych)
nfactors(llltI[-c(1,2)])
# VSS complexity 1 achieves a maximum of 0.56  with  16  factors
# VSS complexity 2 achieves a maximum of 0.66  with  16  factors
# The Velicer MAP achieves a minimum of 0.01  with  1  factors
# Empirical BIC achieves a minimum of  -280.23  with  8  factors
# Sample Size adjusted BIC achieves a minimum of  -135.77  with  9  factors

fa.parallel(llltI[-c(1,2)], n.iter=2000)
# Parallel analysis suggests that the number of factors =  7  and the number of components =  7
## split the difference between sample-size adjusted BIC and parallel analysis with 8:
factorization <- fa(llltI[-c(1,2)], nfactors=8); factorization
# Standardized loadings (pattern matrix) based upon correlation matrix
#                           MR6   MR1   MR2   MR4   MR3   MR5   MR7   MR8     h2    u2 com
# Creativity.self.rating   0.22  0.06 -0.04  0.08 -0.04  0.02 -0.05 -0.14 0.0658 0.934 2.5
# Percentage              -0.05 -0.02  0.01  0.01  0.00 -0.42  0.02  0.02 0.1684 0.832 1.0
# Time.X                  -0.04  0.11  0.04  0.88 -0.02  0.01  0.01  0.02 0.8282 0.172 1.0
# Time.PDF                 0.02  0.99 -0.02  0.04  0.02  0.00 -0.01 -0.01 0.9950 0.005 1.0
# Time.Stats              -0.10  0.21  0.12  0.16 -0.04  0.04  0.12  0.25 0.2310 0.769 4.3
# Time.IRC                 0.01 -0.02  0.99  0.02  0.02  0.01  0.00 -0.01 0.9950 0.005 1.0
# Time.Writing             0.01 -0.02  0.01  0.04 -0.01 -0.03  0.68  0.04 0.4720 0.528 1.0
# Time.Rec                 0.20 -0.12 -0.06  0.42  0.62 -0.02 -0.07 -0.01 0.8501 0.150 2.2
# Time.Music              -0.05  0.05  0.02  0.02 -0.04  0.22  0.02  0.13 0.0909 0.909 2.0
# Time.SRS                -0.07  0.09  0.08  0.00  0.00  0.08  0.06  0.16 0.0702 0.930 3.6
# Time.Sysadmin            0.05 -0.09 -0.04  0.15  0.07  0.01  0.14  0.42 0.2542 0.746 1.7
# Time.Bitcoin             0.45  0.02  0.25 -0.07 -0.03 -0.09 -0.04  0.11 0.3581 0.642 1.9
# Time.Backups             0.22  0.10 -0.08 -0.19  0.12  0.13  0.02  0.27 0.1809 0.819 4.3
# Time.Blackmarkets        0.62 -0.01  0.06 -0.02 -0.09 -0.01 -0.04  0.15 0.4442 0.556 1.2
# Time.Programming         0.06 -0.01 -0.01 -0.04  0.07  0.08  0.41 -0.07 0.1790 0.821 1.3
# Time.DNB                -0.01 -0.01  0.02  0.01 -0.01  0.76 -0.01  0.00 0.5800 0.420 1.0
# Time.Typing             -0.04  0.05  0.02  0.01 -0.02 -0.01  0.00 -0.01 0.0054 0.995 2.9
# Time.Umineko            -0.10  0.08  0.06 -0.15  0.77 -0.01  0.03  0.02 0.5082 0.492 1.2
# Gwern.net.linecount.log  0.65  0.03  0.00 -0.03  0.04  0.00  0.10 -0.13 0.4223 0.578 1.1
# Gwern.net.patches.log    0.11  0.02 -0.01  0.02  0.00  0.06  0.29 -0.06 0.1001 0.900 1.5
#
#                        MR6  MR1  MR2  MR4  MR3  MR5  MR7  MR8
# SS loadings           1.24 1.12 1.13 1.13 1.05 0.86 0.80 0.48
# Proportion Var        0.06 0.06 0.06 0.06 0.05 0.04 0.04 0.02
# Cumulative Var        0.06 0.12 0.17 0.23 0.28 0.33 0.37 0.39
# Proportion Explained  0.16 0.14 0.14 0.14 0.13 0.11 0.10 0.06
# Cumulative Proportion 0.16 0.30 0.45 0.59 0.73 0.84 0.94 1.00
#
#  With factor correlations of
#       MR6   MR1   MR2   MR4   MR3   MR5   MR7  MR8
# MR6  1.00 -0.13  0.26  0.15  0.22 -0.09  0.03 0.16
# MR1 -0.13  1.00 -0.12  0.25 -0.05  0.06  0.12 0.11
# MR2  0.26 -0.12  1.00  0.04 -0.04  0.10  0.20 0.19
# MR4  0.15  0.25  0.04  1.00  0.32  0.01 -0.05 0.10
# MR3  0.22 -0.05 -0.04  0.32  1.00 -0.04 -0.07 0.00
# MR5 -0.09  0.06  0.10  0.01 -0.04  1.00  0.11 0.11
# MR7  0.03  0.12  0.20 -0.05 -0.07  0.11  1.00 0.20
# MR8  0.16  0.11  0.19  0.10  0.00  0.11  0.20 1.00
#
# Mean item complexity =  1.9
# Test of the hypothesis that 8 factors are sufficient.
#
# The degrees of freedom for the null model are  190
# and the objective function was  2.46 with Chi Square of  5344.68
# The degrees of freedom for the model are 58  and the objective function was  0.07
#
# The root mean square of the residuals (RMSR) is  0.02
# The df corrected root mean square of the residuals is  0.03
#
# The harmonic number of observations is  2178 with the empirical chi square  190.08  with prob <  5.9e-16
# The total number of observations was  2178  with MLE Chi Square =  149.65  with prob <  4.9e-10
#
# Tucker Lewis Index of factoring reliability =  0.942
# RMSEA index =  0.027  and the 90 % confidence intervals are  0.022 0.032
# BIC =  -296.15
# Fit based upon off diagonal values = 0.98
# Measures of factor score adequacy
#                                                 MR6  MR1  MR2  MR4  MR3  MR5  MR7   MR8
# Correlation of scores with factors             0.84 1.00 1.00 0.93 0.89 0.80 0.76  0.64
# Multiple R square of scores with factors       0.70 0.99 0.99 0.86 0.79 0.63 0.58  0.41
# Minimum correlation of possible factor scores  0.40 0.99 0.99 0.71 0.58 0.27 0.16 -0.19

lllt$MR6 <- predict(factorization, data=llltI[-c(1,2)])[,1]
lllt$MR1 <- predict(factorization, data=llltI[-c(1,2)])[,2]
lllt$MR7 <- predict(factorization, data=llltI[-c(1,2)])[,7]
lllt$MR8 <- predict(factorization, data=llltI[-c(1,2)])[,8]
l <- lm(cbind(MR6, MR1, MR7, MR8) ~ LLLT, data=lllt); summary(l)
# Response MR6 :
# Coefficients:
#              Estimate Std. Error  t value Pr(>|t|)
# (Intercept) 1.5307773  0.0736275 20.79085  < 2e-16
# LLLTTRUE    0.1319675  0.1349040  0.97823  0.32868
#
# Response MR1 :
# Coefficients:
#               Estimate Std. Error  t value  Pr(>|t|)
# (Intercept) -0.1675241  0.0609841 -2.74701 0.0063473
# LLLTTRUE     0.0317851  0.1117381  0.28446 0.7762378
#
# Response MR7 :
# Coefficients:
#               Estimate Std. Error  t value Pr(>|t|)
# (Intercept) -0.0924052  0.0709438 -1.30251 0.193658
# LLLTTRUE     0.2556655  0.1299869  1.96686 0.050045
#
# Response MR8 :
# Coefficients:
#              Estimate Std. Error t value Pr(>|t|)
# (Intercept) 0.0741850  0.0687618 1.07887  0.28144
# LLLTTRUE    0.1380131  0.1259889 1.09544  0.27413
0.2556655 / sd(lllt$MR7)
# [1] 0.335510445
summary(manova(l))
#            Df     Pillai approx F num Df den Df  Pr(>F)
# LLLT        1 0.01372527 1.127218      4    324 0.34355
# Residuals 327

library(ggplot2)
llltRecent$index <- 1:nrow(llltRecent)
qplot(index, MR7, color=LLLT, data=llltRecent) +
 geom_point(size=I(3)) +
 stat_smooth() +
 scale_colour_manual(values=c("gray49", "green"),
                     name = "LLLT")

Modalert Blind Day Trial

Most people I talk to about modafinil seem to use it for daytime usage; for me that has not ever worked out well, but I had nothing in particular to show against it. So, as I was capping the last of my piracetam-caffeine mix and clearing off my desk, I put the 4 remaining Modalerts pills into capsules with the last of my creatine powder and then mixed them with 4 of the theanine-creatine pills. Like the previous Adderall trial, I will pick one pill blindly each day and guess at the end which it was. If it was active (modafinil-creatine), take a break the next day; if placebo (theanine-creatine), replace the placebo and try again the next day. We’ll see if I notice anything on DNB or possibly Gwern.net edits.

1. Take at 10 AM; seem a bit more active but that could just be the pressure of the holiday season combined with my nice clean desk. I do the chores without too much issue and make progress on other things, but nothing major; I survive going to The Sitter without too much tiredness, so ultimately I decide to give the palm to it being active, but only with 60% confidence. I check the next day, and it was placebo. Oops.

2. Take at 11 AM; distractions ensue and the Christmas tree-cutting also takes up much of the day. By 7 PM, I am exhausted and in a bad mood. While I don’t expect day-time modafinil to buoy me up, I do expect it to at least buffer me against being tired, and so I conclude placebo this time, and with more confidence than yesterday (65%). I check before bed, and it was placebo.

3. 10:30 AM; no major effect that I notice throughout the day - it’s neither good nor bad. This smells like placebo (and part of my mind is going ‘how unlikely is it to get placebo 3 times in a row!’, which is just the Gambler’s fallacy talking inasmuch as this is sampling with replacement). I give it 60% placebo; I check the next day right before taking, and it is. Man!

4. 1 PM; overall this was a pretty productive day, but I can’t say it was very productive. I would almost say even odds, but for some reason I feel a little more inclined towards modafinil. Say 55%. That night’s sleep was vile: the Zeo says it took me 40 minutes to fall asleep, I only slept 7:37 total, and I woke up 7 times. I’m comfortable taking this as evidence of modafinil (half-life 10 hours, 1 PM to midnight is only 1 full halving), bumping my prediction to 75%. I check, and sure enough - modafinil.

5. 10:40 AM; again no major effects, although I got two jQuery extensions working and some additional writing so one could argue the day went well. I don’t know; 50%. Placebo.

6. 11 AM; a rather productive day. I give it 65%. To my surprise, it was placebo.

7. 10 AM; this was an especially productive day, but this was also the day my nicotine gum finally arrived and I just had to try it (I had been waiting so long); it’s definitely a stimulant, alright. But this trashes my own subjective estimates; I hoped it was just placebo, but no, it was modafinil.

8. 9:50 AM; nothing noticed by noon. Managed to finish “Reasons of State: Why Didn’t Denmark Sell Greenland?” which was a surprising amount of work, especially after I managed to delete a third of the first draft - but nothing I would chalk up to modafinil. I decide to give it 60% placebo, and I turn out to be wrong: it was my last modafinil.

So with these 8 results in hand, what do I think? Roughly, I was right 5 of the days and wrong 3 of them. If not for the sleep effect on #4, which is - in a way - cheating (one hopes to detect modafinil due to good effects), the ratio would be 5:4 which is awfully close to a coin-flip. Indeed, a scoring rule ranks my performance at almost identical to a coin flip: -5.49 vs -5.54²¹. (The bright side is that I didn’t do worse than a coin flip: I was at least calibrated.)

I can’t call this much of a success; there may be an effect on my productivity but it’s certainly not very clear subjectively. I’ll chalk this up as a failure for modafinil and evidence for what I believed - day-time modafinil use does not work for me (even if it works for others).

Nuvigil

1. First use

   Took full pill at 10:21 PM when I started feeling a bit tired. Around 11:30, I noticed my head feeling fuzzy but my reading seemed to still be up to snuff. I would eventually finish the science book around 9 AM the next day, taking some very long breaks to walk the dog, write some poems, write a program, do Mnemosyne review (memory performance: subjectively below average, but not as bad as I would have expected from staying up all night), and some other things. Around 4 AM, I reflected that I felt much as I had during my nightwatch job at the same hour of the day - except I had switched sleep schedules for the job. The tiredness continued to build and my willpower weakened so the morning wasn’t as productive as it could have been - but my actual performance when I could be bothered was still pretty normal. That struck me as kind of interesting that I can feel very tired and not act tired, in line with the anecdotes.

   Past noon, I began to feel better, but since I would be driving to errands around 4 PM, I decided to not risk it and take an hour-long nap, which went well, as did the driving. The evening was normal enough that I forgot I had stayed up the previous night, and indeed, I didn’t much feel like going to bed until past midnight. I then slept well, the Zeo giving me a 108 ZQ (not an all-time record, but still unusual).

2. I had intended to run another Adderall trial this day but then I learned we would be going to the midnight showing of the last Harry Potter movie. A perfect opportunity: going to bed at 3 AM after a stimulating battle movie would mean crappy sleep, so why not just do another armodafinil trial and kill 2 birds with one stone?

   I took the pill at 11 PM the evening of (technically, the day before); that day was a little low on sleep than usual, since I had woken up an hour or half-hour early. I didn’t yawn at all during the movie (merely mediocre to my eyes with some questionable parts)²⁴. It worked much the same as it did the previous time - as I walked around at 5 AM or so, I felt perfectly alert. I made good use of the hours and wrote up some stuff.

   (As I was doing this, I reflected how modafinil is such a pure example of the money-time tradeoff. It’s not that you pay someone else to do something for you, which necessarily they will do in a way different from you; nor is it that you have exchanged money to free yourself of a burden of some future time-investment; nor have you paid money for a speculative return of time later in life like with many medical expenses or supplements. Rather, you have paid for 8 hours today of your own time.)

   And as before, around 9 AM I began to feel the peculiar feeling that I was mentally able and apathetic (in a sort of aboulia way); so I decided to try what helped last time, a short nap. But this time, though I took a full hour, I slept not a wink and my Zeo recorded only 2 transient episodes of light sleep! A back-handed sort of proof of alertness, I suppose. I didn’t bother trying again. The rest of the day was mediocre, and I wound up spending much of it on chores and whatnot out of my control. Mentally, I felt better past 3 PM.

   This continued up to 1 AM, at which point I decided not to take a second armodafinil (why spend a second pill to gain what would likely be an unproductive set of 8 hours?) and finish up the experiment with some n-backing. My 5 rounds: 60/38/62/44/50²⁵. This was surprising. Compare those scores with scores from several previous days: 39/42/44/40/20/28/36. I had estimated before the n-backing that my scores would be in the low-end of my usual performance (20-30%) since I had not slept for the past 41 hours, and instead, the lowest score was 38%. If one did not know the context, one might think I had discovered a good nootropic! Interesting evidence that armodafinil preserves at least one kind of mental performance.

3. I stayed up late writing some poems and about how Folding@home kills, and decided to make a night of it. I took the armodafinil at 1 AM; the interesting bit is that this was the morning/evening after what turned out to be an Adderall (as opposed to placebo) trial, so perhaps I will see how well or ill they go together. A set of normal scores from a previous day was 32%/43%/51%/48%. At 11 PM, I scored 39% on DNB; at 1 AM, I scored 50%/43%; 5:15 AM, 39%/37%; 4:10 PM, 42%/40%; 11 PM, 55%/21%/38%. (▂▄▆▅ vs ▃▅▄▃▃▄▃▇▁▃)

   The peculiar tired-sharp feeling was there as usual, and the DNB scores continue to suggest this is not an illusion, as they remain in the same 30-50% band as my normal performance. I did not notice the previous ‘aboulia’ feeling; instead, around noon, I was filled with a nervous energy and a disturbingly rapid pulse which meditation & deep breathing did little to help with, and which didn’t go away for an hour or so. Fortunately, this was primarily at church, so while I felt irritable, I didn’t actually interact with anyone or snap at them, and was able to keep a lid on it. I have no idea what that was about. I wondered if it might’ve been a serotonin storm since amphetamines are some of the drugs that can trigger storms but the Adderall had been at 10:50 AM the previous day, or >25 hours (the half-lives of the ingredients being around 13 hours). An hour or two previously I had taken my usual caffeine-piracetam pill with my morning tea - could that have interacted with the armodafinil and the residual Adderall? Or was it caffeine+modafinil? Speculation, perhaps. A house-mate was ill for a few hours the previous day, so maybe the truth is as prosaic as me catching whatever he had.

4. Stayed up with the purpose of finishing my work for a contest. This time, instead of taking the pill as a single large dose (I feel that after 3 times, I understand what it’s like), I will take 4 doses over the new day. I took the first quarter at 1 AM, when I was starting to feel a little foggy but not majorly impaired. Second dose, 5:30 AM; feeling a little impaired. 8:20 AM, third dose; as usual, I feel physically a bit off and mentally tired - but still mentally sharp when I actually do something. Early on, my heart rate seemed a bit high and my limbs trembling, but it’s pretty clear now that that was the caffeine or piracetam. It may be that the other day, it was the caffeine’s fault as I suspected. The final dose was around noon. The afternoon ‘crash’ wasn’t so pronounced this time, although motivation remains a problem. I put everything into finishing up the spaced repetition literature review, and didn’t do any n-backing until 11:30 PM: 32/34/31/54/40%.

5. With the last pill, I wound up trying split-doses on non-full nights; that is, if one full pill keeps me awake one full night, what does 1/4th the pill do?

   1. Between midnight and 1:36 AM, I do four rounds of n-back: 50/39/30/55%. I then take 1/4th of the pill and have some tea. At roughly 1:30 AM, AngryParsley linked a SF anthology/novel, Fine Structure, which sucked me in for the next 3-4 hours until I finally finished the whole thing. At 5:20 AM, circumstances forced me to go to bed, still having only taken 1/4th of the pill and that determines this particular experiment of sleep; I quickly do some n-back: 29/20/20/54/42. I fall asleep in 13 minutes and sleep for 2:48, for a ZQ of 28 (a full night being ~100). I did not notice anything from that possible modafinil+caffeine interaction. Subjectively upon awakening: I don’t feel great, but I don’t feel like 2-3 hours of sleep either. N-back at 10 AM after breakfast: 25/54/44/38/33. These are not very impressive, but seem normal despite taking the last armodafinil ~9 hours ago; perhaps the 3 hours were enough. Later that day, at 11:30 PM (just before bed): 26/56/47.

   2. 2 break days later, I took the quarter-pill at 11:22 PM. I had discovered I had for years physically possessed a very long interview not available online, and transcribing that seemed like a good way to use up a few hours. I did some reading, some Mnemosyne, and started it around midnight, finishing around 2:30 AM. There seemed a mental dip around 30 minutes after the armodafinil, but then things really picked up and I made very good progress transcribing the final draft of 9000 words in that period. (In comparison, “The Conscience of the Otaking” parts 2 & 4 were much easier to read than the tiny font of the RahXephon booklet, took perhaps 3 hours, and totaled only 6500 words. The nicotine is probably also to thank.) By 3:40 AM, my writing seems to be clumsier and my mind fogged. Began DNB at 3:50: 61/53/44. Went to bed at 4:05, fell asleep in 16 minutes, slept for 3:56. Waking up was easier and I felt better, so the extra hour seemed to help.

   3. With this experiment, I broke from the previous methodology, taking the remaining and final half Nuvigil at midnight. I am behind on work and could use a full night to catch up. By 8 AM, I am as usual impressed by the Nuvigil - with Modalert or something, I generally start to feel down by mid-morning, but with Nuvigil, I feel pretty much as I did at 1 AM. Sleep: 9:51/9:15/8:27

Waklert

I noticed on SR something I had never seen before, an offer for 150mgx10 of “Waklert” for ₿13.47 (then, ₿1 = $3.14). I searched and it seemed Sun was somehow manufacturing armodafinil! Interesting. Maybe not cost-effective, but I tried out of curiosity. They look and are packaged the same as the Modalert, but at a higher price-point: 150 rather than 81 rupees. Not entirely sure how to use them: assuming quality is the same, 150mg Waklert is still 100mg less armodafinil than the 250mg Nuvigil pills.

1. Take quarter at midnight, another quarter at 2 AM. Night runs reasonably well once I remember to eat a lot of food (I finish a big editing task I had put off for weeks), but the apathy kicks in early around 4 AM so I gave up and watched Scott Pilgrim vs. the World, finishing around 6 AM. I then read until it’s time to go to a big shotgun club function, which occupies the rest of the morning and afternoon; I had nothing to do much of the time and napped very poorly on occasion. By the time we got back at 4 PM, the apathy was completely gone and I started some modafinil research with gusto (interrupted by going to see Puss in Boots). That night: Zeo recorded 8:30 of sleep, gap of about 1:50 in the recording, figure 10:10 total sleep; following night, 8:33; third night, 8:47; fourth, 8:20 (▇▁▁▁).

2. First quarter at 1:20 AM. Second quarter at 4 AM. 20 minute nap at 7:30 AM; took show and last 2 doses at 11 AM. (If I feel bad past 3 PM, I’ll try one of the Modalerts or maybe another quarter of a Waklert - 150mg may just be too little.) Overall, pretty good day. Nights: 9:43; 9:51; 7:57; 8:25; 8:08; 9:02; 8:07 (▇█▁▂▁▄▁).

3. First half at 6 AM; second half at noon. Wrote a short essay I’d been putting off and napped for 1:40 from 9 AM to 10:40. This approach seems to work a little better as far as the aboulia goes. (I also bother to smell my urine this time around - there’s a definite off smell to it.) Nights: 10:02; 8:50; 10:40; 7:38 (2 bad nights of nasal infections); 8:28; 8:20; 8:43 (▆▃█▁▂▂▃).

4. Whole pill at 5:42 AM. (Somewhat productive night/morning beforehand.) DNB at 2 PM: 52/36/54 (▇▁█); slept for 49 minutes; DNB at 8 PM: 50/44/38/40 (▆▄▁▂). Nights: 10:02; 8:02; no data; 9:21; 8:20 (█▁ ▅▂).

5. Whole pill at 3 AM. I spend the entire morning and afternoon typing up a transcript of “Earth in My Window”. I tried taking a nap around 10 AM, but during the hour I was down, I had <5m of light sleep, the Zeo said. After I finished the transcript (~16,600 words with formatting), I was completely pooped and watched a bunch of Mobile Suit Gundam episodes, then I did Mnemosyne. The rest of the night was nothing to write home about either - some reading, movie watching, etc. Next time I will go back to split-doses and avoid typing up 110kB of text. On the positive side, this is the first trial I had available the ‘average daily grade’ Mnemosyne 2.0 plugin. The daily averages all are 3-point-something (peaking at 3.89 and flooring at 3.59), so just graphing the past 2 weeks, the modafinil day, and recovery days: ▅█▅▆▄▆▄▃▅▄▁▄▄ ▁ ▂▄▄█. Not an impressive performance but there was a previous non-modafinil day just as bad, and I’m not too sure how important a metric this is; I must see whether future trials show similar underperformance. Nights: 11:29; 9:22; 8:25; 8:41.

6. Spaced repetition at midnight: 3.68. (Graphing preceding and following days: ▅▄▆▆▁▅▆▃▆▄█ ▄ ▂▄▄▅) DNB starting 12:55 AM: 30/34/41. Transcribed Sawaragi2005, then took a walk. DNB starting 6:45 AM: 45/44/33. Decided to take a nap and then take half the armodafinil on awakening, before breakfast. I wound up oversleeping until noon (4:28); since it was so late, I took only half the armodafinil sublingually. I spent the afternoon learning how to do “value of information” calculations, and then carefully working through 8 or 9 examples for my various pages, which I published on Lesswrong. That was a useful little project. DNB starting 12:09 AM: 30/38/48. (To graph the preceding day and this night: ▇▂█▆▅▃▃▇▇▇▁▂▄ ▅▅▁▁▃▆) Nights: 9:13; 7:24; 9:13; 8:20; 8:31.

7. Feeling behind, I resolved to take some armodafinil the next morning, which I did - but in my hurry I failed to recall that 200mg armodafinil was probably too much to take during the day, with its long half life. As a result, I felt irritated and not that great during the day (possibly aggravated by some caffeine - I wish some studies would be done on the possible interaction of modafinil and caffeine so I knew if I was imagining it or not). Certainly not what I had been hoping for. I went to bed after midnight (half an hour later than usual), and suffered severe insomnia. The time wasn’t entirely wasted as I wrote a short story and figured out how to make nicotine gum placebos during the hours in the dark, but I could have done without the experience. All metrics omitted because it was a day usage.

Poor Absorption?

2 commenters point out that my possible lack of result is due to my mistaken assumption that if nicotine is absorbable through skin, mouth, and lungs it ought to be perfectly fine to absorb it through my stomach by drinking it (rather than vaporizing it and breathing it with an e-cigarette machine) - it’s apparently known that absorption differs in the stomach.

• the online book The Cigarette Papers describes early animal experiments (without specific bioavailability percentages):

  …The Fate of Nicotine in the Body also describes Battelle’s animal work on nicotine absorption. Using C¹⁴-labeled nicotine in rabbits, the Battelle scientists compared gastric absorption with pulmonary absorption. Gastric absorption was slow, and first pass removal of nicotine by the liver (which transforms nicotine into inactive metabolites) was demonstrated following gastric administration, with consequently low systemic nicotine levels. In contrast, absorption from the lungs was rapid and led to widespread distribution. These results show that nicotine absorbed from the stomach is largely metabolized by the liver before it has a chance to get to the brain. That is why tobacco products have to be puffed, smoked or sucked on, or absorbed directly into the bloodstream (ie. via a nicotine patch). A nicotine pill would not work because the nicotine would be inactivated before it reached the brain.

• “Metabolism and Disposition Kinetics of Nicotine”:

  Absorption of nicotine across biological membranes depends on pH. Nicotine is a weak base with a pKa of 8.0 (Fowler, 1954_70ya). In its ionized state, such as in acidic environments, nicotine does not rapidly cross membranes…About 80 to 90% of inhaled nicotine is absorbed during smoking as assessed using C¹⁴-nicotine (Armitage et al 1975_49ya). The efficacy of absorption of nicotine from environmental smoke in nonsmoking women has been measured to be 60 to 80% (Iwase et al 1991_33ya)…The various formulations of nicotine replacement therapy (NRT), such as nicotine gum, transdermal patch, nasal spray, inhaler, sublingual tablets, and lozenges, are buffered to alkaline pH to facilitate the absorption of nicotine through cell membranes. Absorption of nicotine from all NRTs is slower and the increase in nicotine blood levels more gradual than from smoking (Table 1). This slow increase in blood and especially brain levels results in low abuse liability of NRTs (Henningfield and Keenan, 1993_31ya; West et al 2000_24ya). Only nasal spray provides a rapid delivery of nicotine that is closer to the rate of nicotine delivery achieved with smoking (Sutherland et al 1992_32ya; Gourlay and Benowitz, 1997_27ya; Guthrie et al 1999_25ya). The absolute dose of nicotine absorbed systemically from nicotine gum is much less than the nicotine content of the gum, in part, because considerable nicotine is swallowed with subsequent first-pass metabolism (Benowitz et al 1987_37ya). Some nicotine is also retained in chewed gum. A portion of the nicotine dose is swallowed and subjected to first-pass metabolism when using other NRTs, inhaler, sublingual tablets, nasal spray, and lozenges (Johansson et al 1991_33ya; Bergstrom et al 1995_29ya; Lunell et al 1996_28ya; Molander and Lunell, 2001_23ya; Choi et al 2003_21ya). Bioavailability for these products with absorption mainly through the mucosa of the oral cavity and a considerable swallowed portion is about 50 to 80% (Table 1)…Nicotine is poorly absorbed from the stomach because it is protonated (ionized) in the acidic gastric fluid, but is well absorbed in the small intestine, which has a more alkaline pH and a large surface area. Following the administration of nicotine capsules or nicotine in solution, peak concentrations are reached in about 1 h (Benowitz et al 1991_33ya; Zins et al 1997_27ya; Dempsey et al 2004_20ya). The oral bioavailability of nicotine is about 20 to 45% (Benowitz et al 1991_33ya; Compton et al 1997_27ya; Zins et al 1997_27ya). Oral bioavailability is incomplete because of the hepatic first-pass metabolism. Also the bioavailability after colonic (enema) administration of nicotine (examined as a potential therapy for ulcerative colitis) is low, around 15 to 25%, presumably due to hepatic first-pass metabolism (Zins et al 1997_27ya). Cotinine is much more polar than nicotine, is metabolized more slowly, and undergoes little, if any, first-pass metabolism after oral dosing (Benowitz et al 1983_41yab; De Schepper et al 1987_37ya; Zevin et al 1997_27ya).

  Particularly germane is the table of absorption by administration methods, which gives bioavailability for oral capsule (44%) and oral solution (20%)

• “An oral formulation of nicotine for release and absorption in the colon: its development and pharmacokinetics” does not break out bioavailability for their enema, but they seem to have measured levels consistent with 10-20%.

• “Absorption of nicotine by the human stomach and its effect on gastric ion fluxes and potential difference”’s abstract confirms the variation from acidity:

  Nicotine was well absorbed, mean 18.6±3.4% in 15 min, on intragastric instillation at pH 9.8. Absorption was accompanied by side effects of nausea and vomiting, and delay in gastric emptying. Gastric absorption of nicotine at pH 7.4 was less marked (mean 8.2±2.9%), but was negligible at pH 1 (mean 3.3±1.4%).

• “Facts About Nicotine Toxicity”:

  Nicotine is poorly absorbed from the stomach due to the acidity of the gastric fluid, but is well absorbed in the small intestine, which has a more alkaline pH and a large surface area [“Nicotine, its metabolism and an overview of its biological effects”].

• Tobacco and Shamanism in South America (Wilbert 1993_31ya), pg 139:

  Nicotine absorption through the stomach is variable and relatively reduced in comparison with absorption via the buccal cavity and the small intestine. ‘Drinking’, ‘eating’, and swallowing of tobacco smoke by South American Indians have frequently been reported. Tenetehara shamans reach a state of tobacco narcosis through large swallows of smoke, and Tapirape shams are said to “eat smoke” by forcing down large gulps of smoke only to expel it again in a rapid sequence of belches. In general, swallowing of tobacco smoke is quite frequently likened to ‘drinking’. However, although the amounts of nicotine swallowed in this way - or in the form of saturated saliva or pipe juice - may be large enough to be behaviorally significant at normal levels of gastric pH, nicotine, like other weak bases, is not significantly absorbed.

  From the standpoint of absorption, the drinking of tobacco juice and the interaction of the infusion or concoction with the small intestine is a highly effective method of gastrointestinal nicotine administration. The epithelial area of the intestines is incomparably larger than the mucosa of the upper tract including the stomach, and “the small intestine represents the area with the greatest capacity for absorption” (Levine 1983_41ya:81-83). As practiced by most of the sixty-four tribes documented here, intoxicated states are achieved by drinking tobacco juice through the mouth and/or nose…The large intestine, although functionally little equipped for absorption, nevertheless absorbs nicotine that may have passed through the small intestine.

• “Stomach absorption of intubated insecticides in fasted mice”’s abstract reports 10% stomach bioavailability in rats.

It looks like the overall picture is that nicotine is absorbed well in the intestines and the colon, but not so well in the stomach; this might be the explanation for the lack of effect, except on the other hand, the specific estimates I see are that 10-20% of the nicotine will be bioavailable in the stomach (as compared to 50%+ for mouth or lungs)… so any of my doses of >5ml should have overcome the poorer bioavailability! But on the gripping hand, these papers are mentioning something about the liver metabolizing nicotine when absorbed through the stomach, so…

Experiment

logBinaryScore = sum . map (\(result,p) -> if result then 1 + logBase 2 p else 1 + logBase 2 (1-p))
logBinaryScore [(True,0.35),(False,0.40),(False,0.40),(True,0.60),(True,0.35),(False,0.45),(False,0.50),
                (True,0.60),(False,0.30),(True,0.50),(False,0.40),(False,0.30),(False,0.25),(False,0.75),
                (False,0.40),(False,0.40),(False,0.65),(False,0.45),(True,0.50),(False,0.65),(True,0.40),
                (True,0.55),(True,0.40),(False,0.50),(False,0.60),(True,0.40),(False,0.50),(False,0.50),
                (False,0.55),(True,0.55),(False,0.50),(False,0.55),(False,0.45),(True,0.55),(True,0.50),
                (True,0.50),(False,0.55),(True,0.50)]
-- -0.58

## individual rounds; the imbalance is unfortunate but the experiment design means nothing can be done
on <- c(36,36,25,27,38,50,34,62,33,22,40,28,37,50,25,42,44,58,47,55,38,35,43,60,47,44,40,33,44,
        19,58,38,41,52,41,33,47,45,45,55,45,27,35,45,30,30,52,36,28,43,50,27,29,55,45,31,15,47,
        64,35,33,60,38,28,60,45,64,50,44,38,35,61,56,30,44,41,37,41,43,38)
off <- c(25,34,30,40,57,34,41,51,36,26,37,42,40,45,31,24,38,40,47,35,31,27,66,25,17,43,46,50,36,
         38,58,50,23,50,31,38,33,66,30,68,42,40,29,69,45,60,37,22,28,40,41,45,37,18,50,20,41,42,
         47,44,60,31,46,46,55,47,42,35,40,29,47,56,37,50,20,31,42,53,27,45,50,65,33,33,33,40,47,
         41,25,55,40,31,30,45,50,20,25,30,70,47,47,42,40,35,45,60,37,22,38,36,54,64,25,28,50,42,
         31,50,30,30)
on2 <- rowMeans(as.data.frame(matrix(on,ncol=5,byrow=TRUE)))
off2 <- rowMeans(as.data.frame(matrix(off,ncol=5,byrow=TRUE)))
on2
#  [1] 32.4 40.2 36.0 49.2 44.6 36.0 46.0 45.0 36.4 37.8 41.2 38.4 43.8 48.2 45.2
# [16] 40.0
off2
#  [1] 37.2 37.6 39.0 36.8 33.2 42.6 42.4 47.0 45.0 37.4 38.2 38.8 47.6 38.6 42.0
# [16] 39.6 42.8 41.6 39.2 38.4 41.8 38.6 44.2 36.6

source("BEST.R")
mcmc = BESTmcmc(on2, off2); postInfo
#            SUMMARY.INFO
# PARAMETER         mean     median       mode     HDIlow   HDIhigh pcgtZero
#   mu1       41.2808129 41.2819208 41.2272636 38.5078129 44.032699       NA
#   mu2       40.1981087 40.1955543 40.1777039 38.6810806 41.706469       NA
#   muDiff     1.0827042  1.0837831  1.1279921 -2.0292432  4.244909 75.87121
#   sigma1     5.2563674  5.0898354  4.7768681  3.3307493  7.511054       NA
#   sigma2     3.5513796  3.4850902  3.3453379  2.4655024  4.782887       NA
#   sigmaDiff  1.7049879  1.5917839  1.3816030 -0.6523817  4.300692 93.36015
#   nu        37.7948193 29.3217989 13.0664336  2.2755711 98.116623       NA
#   nuLog10    1.4472479  1.4671906  1.5204474  0.7604963  2.101837       NA
#   effSz      0.2460061  0.2450074  0.2361248 -0.4399959  0.936570 75.87121

var.test(on2, off2, alternative="greater")
#     F test to compare two variances
#
# data:  on2 and off2
# F = 1.9823, num df = 15, denom df = 23, p-value = 0.06775
# alternative hypothesis: true ratio of variances is greater than 1
# 95% confidence interval:
#  0.9314525       Inf
# sample estimates:
# ratio of variances
#           1.982333

mcmc = BESTmcmc(on,off); postInfo
#            SUMMARY.INFO
# PARAMETER          mean      median        mode     HDIlow     HDIhigh pcgtZero
#   mu1       41.22703657 41.22582276 41.11576792 38.7591670  43.7209215       NA
#   mu2       40.12386083 40.12235449 40.04585340 37.9655703  42.3037602       NA
#   muDiff     1.10317574  1.10302023  1.13446641 -2.1520680   4.4246013 74.52276
#   sigma1    10.91966242 10.86603052 10.74158135  9.1335897  12.7962565       NA
#   sigma2    11.69484205 11.66111990 11.57560017 10.1050885  13.3605913       NA
#   sigmaDiff -0.77517964 -0.79214849 -0.85774274 -3.1789680   1.6252535 25.70744
#   nu        46.86258782 38.65278685 22.91066668  5.8159908 109.9850644       NA
#   nuLog10    1.57972151  1.58718081  1.60810992  1.0214182   2.1234248       NA
#   effSz      0.09778545  0.09763823  0.09931263 -0.1895882   0.3907156 74.52276

var.test(on, off, alternative="greater")
#     F test to compare two variances
#
# data:  on and off
# F = 0.8564, num df = 79, denom df = 119, p-value = 0.7689
# alternative hypothesis: true ratio of variances is greater than 1
# 95% confidence interval:
#  0.6140736       Inf
# sample estimates:
# ratio of variances
#           0.856387

nicotine <- read.table(stdin(),header=TRUE)
day      active mp score
20120824 1      3  35.2
20120827 0      5  37.2
20120828 0      3  37.6
20120830 1      3  37.75
20120831 1      2  37.75
20120902 0      2  36.0
20120905 0      5  36.0
20120906 1      5  37.25
20120910 0      5  49.2
20120911 1      3  36.8
20120912 0      3  44.6
20120913 0      5  38.4
20120915 0      5  43.8
20120916 0      2  39.6
20120918 0      3  49.6
20120919 0      4  38.4
20120923 0      5  36.2
20120924 0      5  45.4
20120925 1      3  43.8
20120926 0      4  36.4
20120929 1      3  43.8
20120930 1      3  36.0
20121001 1      3  46.0
20121002 0      4  45.0
20121008 0      2  34.6
20121009 1      3  45.2
20121012 0      5  37.8
20121013 0      4  37.2
20121016 0      4  40.2
20121020 1      3  39.0
20121021 0      3  41.2
20121022 0      3  42.2
20121024 0      5  40.4
20121029 1      2  41.4
20121031 1      3  38.4
20121101 1      5  43.8
20121102 0      3  48.2
20121103 1      5  40.6
summary(nicotine)
#       day               active             mp            score
#  Min.   :20120824   Min.   :0.0000   Min.   :2.000   Min.   :34.60
#  1st Qu.:20120911   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:37.21
#  Median :20120926   Median :0.0000   Median :3.000   Median :39.30
#  Mean   :20120954   Mean   :0.3947   Mean   :3.632   Mean   :40.47
#  3rd Qu.:20121015   3rd Qu.:1.0000   3rd Qu.:5.000   3rd Qu.:43.80
#  Max.   :20121103   Max.   :1.0000   Max.   :5.000   Max.   :49.60
cor(nicotine)
#               day      active          mp       score
# day                0.05331968  0.07437166  0.32021554
# active                        -0.27754064 -0.05727501
# mp                                         0.05238032

Power

I don’t expect to find an effect, though; a quick t-test power analysis of a one-sided paired design with 23 pairs suggests that a reasonable power of 80% would still only be able to detect an increase of d>=0.5:

pwr.t.test(n=23, type="paired", alternative="greater", sig.level=0.05, power=0.8)
#      Paired t test power calculation
#
#               n = 23
#               d = 0.5352

Or in other words, since the standard deviation of my previous self-ratings is 0.75 (see the Weather and my productivity data), a mean rating increase of >0.39 on the self-rating. This is, unfortunately, implying an extreme shift in my self-assessments (for example, 3s are ~50% of the self-ratings and 4s ~25%; to cause an increase of 0.25 while leaving 2s alone in a sample of 23 days, one would have to push 3s down to ~25% and 4s up to ~47%). So in advance, we can see that the weak plausible effects for Noopept are not going to be detected here at our usual statistical levels with just the sample I have (a more plausible experiment might use 178 pairs over a year, detecting down to d>=0.18). But if the sign is right, it might make Noopept worthwhile to investigate further. And the hardest part of this was just making the pills, so it’s not a waste of effort.

Data

Available as a CSV spanning 15 May - 2013-07-09, with magnesium l-threonate consumption as a covariate (see the magnesium page).

Analysis

Some quick tests turn in similar conclusions: both Noopept and the “Magtein” increased self-rating but not statistically-significantly (as expected from the beginning due to the lack of power).

npt <- read.csv("https://gwern.net/doc/nootropic/quantified-self/2013-gwern-noopept.csv")
wilcox.test(MP ~ Noopept, alternative="less", data = npt)
#
#     Wilcoxon rank sum test with continuity correction
#
# data:  MP by Noopept
# W = 343, p-value = 0.2607
summary(lm(MP ~ Noopept + Magtein, data = npt))
# ...Coefficients:
#             Estimate Std. Error t value Pr(>|t|)
# (Intercept)   2.8038     0.1556   18.02   <2e-16
# Noopept       0.0886     0.2098    0.42     0.67
# Magtein       0.2673     0.2070    1.29     0.20
#
# Residual standard error: 0.761 on 53 degrees of freedom
# Multiple R-squared:  0.0379,    Adjusted R-squared:  0.00164
# F-statistic: 1.05 on 2 and 53 DF,  p-value: 0.359

More specifically, the ordinal logistic regression estimates effect sizes of odds-ratio 1.3 for the Noopept and 1.9 for the magnesium:

library(rms)
npt$MP <- as.ordered(npt$MP)
lmodel <- lrm(MP ~ Noopept + Magtein, data = npt); lmodel
# ...
#         Coef    S.E.   Wald Z Pr(>|Z|)
# y>=3     0.4330 0.4049  1.07  0.2849
# y>=4    -1.4625 0.4524 -3.23  0.0012
# Noopept  0.2336 0.5114  0.46  0.6479
# Magtein  0.6748 0.5098  1.32  0.1856

The magnesium was neither randomized nor blinded and included mostly as a covariate to avoid confounding (the Noopept coefficient & t-value increase somewhat without the Magtein variable), so an OR of 1.9 is likely too high; in any case, this experiment was too small to reliably detect any effect (~26% power, see bootstrap power simulation in the magnesium section) so we can’t say too much.

set.seed(3333)
library(boot)
noopeptPower <- function(dt, indices) {
    d <- dt[indices,] # bootstrap's _n_ = original _n_
    lmodel <- lrm(MP ~ Noopept + Magtein, data = d)
    return(anova(lmodel)[7]) # _p_-value for the Noopept coefficient
}
bs <- boot(data=npt, statistic=noopeptPower, R=100000, parallel="multicore", ncpus=4)
alpha <- 0.05
print(sum(bs$t<=alpha) / length(bs$t))
# [1] 0.073

So for the observed effect size, the small Noopept sample had only 7% power to turn in a statistically-significant result. Given the plausible effect size, and weakness of the experiment, I find these results encouraging.

Design

In avoiding experimenting with more Russian Noopept pills and using instead the easily-purchased powder form of Noopept, there are two opposing considerations: Russian Noopept is reportedly the best, so we might expect anything I buy online to be weaker or impure or inferior somehow and the effect size smaller than in the pilot experiment; but by buying my own supply & using powder I can double or triple the dose to 20mg or 30mg (to compensate for the original under-dosing of 10mg) and so the effect size larger than in the pilot experiment.

As it happened, Health Supplement Wholesalers (since renamed Powder City) offered me a sample of their products, including their 5g Noopept powder ($13). I’d never used HSW before & they had some issues in the past; but I haven’t seen any recent complaints, so I was willing to try them. My 5g from batch #130830 arrived quickly (photos: packaging, powder contents). I tried some (tastes just slightly unpleasant, like an ultra-weak piracetam), and I set about capping the fluffy white flour-like powder with the hilariously tiny scoop they provide.

It took 4 hours to cap 432 Noopept pills and another 432 flour pills. I tried to allocate the Noopept as evenly as possible (3 little scoops per pill) which the HSW packaging suggested would be 10-30mg; running out after 432 implies I managed to get ~12mg into each ($\frac{5000}{432}=11.6$). At 2 pills a day, the experiment will run under a year.

I don’t want to synchronize with the magnesium or lithium experiments, so I’ll use paired blocks of 3 days randomized 50:50, which will help with the reported tolerance of Noopept setting in after a few days and one needing to ‘cycle’.

To make things more interesting, I think I would like to try randomizing different dosages as well: 12mg, 24mg, and 36mg (1-3 pills); on 2014-05-05, because I wanted to finish up the experiment earlier, I decided to add 2 larger doses of 48 & 60mg (4-5 pills) as options. Then I can include the previous pilot study as 10mg doses, and regress over dose amount.

During this time period, I generally refrained from using any nicotine (I wound up using it only 3x in the experimental period) or modafinil (0x) to avoid adding variation to results. I did use magnesium citrate & LLLT (discussed later). Finally, I was taking a stack like this:

1. 1mg melatonin at bedtime

2. 5000IU vitamin D & multivitamin at morning; an iron supplement every 3 days

3. from 25 March to 2014-09-18, ~5g of creatine monohydrate per day

4. a few times a day, taking a custom gel pill which in total supplies ~1g piracetam & 200mg caffeine

library(boot)
library(rms)
npt <- read.csv("https://gwern.net/doc/nootropic/quantified-self/2013-gwern-noopept.csv")
newNoopeptPower <- function(dt, indices) {
    d <- dt[sample(nrow(dt), n, replace=TRUE), ] # new dataset, possibly larger than the original
    lmodel <- lrm(MP ~ Noopept + Magtein, data = d)
    return(anova(lmodel)[7])
}
alpha <- 0.05
for (n in seq(from = 300, to = 600, by = 30)) {
   bs <- boot(data=npt, statistic=newNoopeptPower, R=10000, parallel="multicore", ncpus=4)
   print(c(n, sum(bs$t<=alpha)/length(bs$t)))
}
# 0.18/0.19/0.21/0.21/0.23/0.25/0.26/0.28/0.29/0.32/0.32

npt[1,2] <- npt[1,2] + 1
npt[2,2] <- npt[2,2] + 1
n <- 530
bs <- boot(data=npt, statistic=newNoopeptPower, R=100000, parallel="multicore", ncpus=4)
print(c(n, sum(bs$t<=alpha)/length(bs$t)))
# [1] 530.0000   0.8241

Data

1. 20mg: 15 September - 17 September: 0

   18 - 20 September: 1

2. 30mg: 21 September - 23? September: 1

   24 - 26: 0

3. 20mg: 27 - 29 September: 0

   30 - 2 October: 1

4. 10mg: 3 - 5 October: 1

   6 - 8 October: 0

5. 30mg: 9 - 11 October: 1

   12 - 14 October: 0

6. 10mg: 15 - 17 October: 1

   18 - 20 October: 0

7. 30mg: 22 - 24 October: 1

   25 - 27 October: 0

8. 10mg: 28 - 30 October: 1

   31 - 2 November: 0

9. 30mg: 4 - 6 November: 1

   7 - 9 November: 0

10. 20mg: 11 - 13 Nov: 0

    14 - 16 Nov: 1

11. 30mg: 20 - 22 November: 1

    23 - 25 November: 0

12. 20mg: 26 - 28 November: 1

    29 - 1 December: 0

13. 30mg: 2 - 4 December: 1

    5 - 7 December: 0

14. 10mg: 8 - 10 December: 1

    11 - 13 December: 0

15. 30mg: 14 - 16 December: 0

    17 - 19 December: 1

16. 20mg: 20 - 22 December: 0

    27 - 29 December: 1

17. 10mg: 1 - 2014-01-03: 1

    4 - 2014-01-06: 0

18. 30mg: 7 - 9 January: 1

    10 - 12 January: 0

19. 10mg: 13 - 15 January: 1

    16 - 17 January: 0

20. 20mg: 18 - 20 January: 0

    21 - 23 January: 1

21. 30mg: 25 - 27 January: 0

    28 - 30 January: 1

22. 10mg: 31 January - 2 February: 1

    3 - 5 February: 0

23. 30mg: 8 - 10 February: 1

    11 - 13 February: 1

24. 10mg: 14 - 16 February: 0

    17 - 19 February: 1

25. 30mg: 20 - 22 February: 1

    22 - 25 February: 0

26. 20mg: 26 - 28 February: 0

    1 March - 3 March: 1

27. 10mg: 4 March - 6 March: 1

    7 March - 9 March: 0

28. 30mg: 10 - 11 March: 0; accidentally unblinded & restarted on the 12th (a spare rice placebo, which is visibly different from the flour/Noopept capsules, was mixed in)

29. 30mg: 12 - 14 March : 1

    15 - 17 March: 0

30. 20mg: 18 - 20 March: 0

    21 - 23 March: 1

31. 10mg: 24 - 26 March: 1

    27 - 29 March: 0

32. 20mg: 30 - 1 April: 0

    2 - 4 April: 1

33. 10mg: 5 - 7 April: 1

    8 - 10 April: 0

34. 30mg: 11 - 13 April: 1

    14 - 16 April: 0

35. 20mg: 17 - 19 April: 0

    20 - 22 April: 1

36. 10mg: 23 - 25 April: 1

    26 - 28 April: 0

37. 30mg: 29 - 1 May: 1

    2 - 4 May: 0

38. 48mg: 5 - 7 May: 0

    8 - 10 May: 1

39. 60mg: 11 - 13 May: 0

    14 - 17 May: 1

40. 20mg: 18 - 20 May: 0

    21 - 23 May: 1

41. 48mg: 24 - 26 May: 1

    27 - 29 May: 0

42. 60mg: 30 - 1 June: 0

    2 - 4 June: 1

43. 30mg: 5 - 7 June: 1

    8 - 10 June: 0

44. 5x: 11 - 13 June: 1

    14 - 16 June: 0

45. 3x: 17 - 19 June: 0

    20 - 22 June: 1

46. 5x: 23 - 25 June: 0

    26 - 28 June: 1

47. 4x: 29 June - 1 July: 1

    2 - 4 July: 0

48. 3x: 5 - 7 July: 1

    8 - 9 July: 0

49. 5x: 10 - 12 July: 1

    13 - 15 July: 0

50. 3x: 16 - 18 July: 0

    19 - 21 July: 1

51. 4x: 23 - 25 July: 0

    26 - 28 July: 1

52. 5x: 29 - 31 July: 0

    1 - 3 August: 1

53. 3x: 4 - 6 August: 1

    7 - 9 August: 0

54. 3x: 10 - 12 August: 0

    13 - 15 August: 1

55. 3x: 16 - 18 August: 0

    19 - 21 August: 1

56. 2x: 23 - 25 August: 0

    26 - 28 August: 1

Analysis

Analyzing the results is a little tricky because I was simultaneously running the first magnesium citrate self-experiment, which turned out to cause a quite complex result which looks like a gradually-accumulating overdose negating an initial benefit for net harm, and also toying with LLLT, which turned out to have a strong correlation with benefits. So for the potential small Noopept effect to not be swamped, I need to include those in the analysis. I designed the experiment to try to find the best dose level, so I want to look at an average Noopept effect but also the estimated effect at each dose size in case some are negative (especially in the case of 5-pills/60mg); I included the pilot experiment data as 10mg doses since they were also blind & randomized. Finally, ‘missingness’ affects analysis: because not every variable is recorded for each date (what was the value of the variable for the blind randomized magnesium citrate before and after I finished that experiment? what value do you assign the Magtein variable before I bought it and after I used it all up?), just running a linear regression may not work exactly as one expects as various days get omitted because part of the data was missing.

noopeptSecond <- read.csv("https://gwern.net/doc/nootropic/quantified-self/2013-2014-gwern-noopept.csv", colClasses=c("Date","integer","integer","integer","logical"))
l <- lm(MP ~ Noopept +
             LLLT +
             as.logical(Magnesium.citrate) + as.integer(Date) + as.logical(Magnesium.citrate):as.integer(Date),
        data=noopeptSecond)
summary(l)
# Coefficients:
#                                                        Estimate   Std. Error  t value   Pr(>|t|)
# (Intercept)                                        24.254373177 14.252905125  1.70171 0.09043607
# Noopept                                             0.002069507  0.003937337  0.52561 0.59976836
# LLLTTRUE                                            0.330112028  0.096133360  3.43390 0.00072963
# as.logical(Magnesium.citrate)TRUE                  27.058060337 19.655569654  1.37661 0.17024431
# as.integer(Date)                                   -0.001313316  0.000886616 -1.48127 0.14018300
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.001699162  0.001222719 -1.38966 0.16625033
#
# Residual standard error: 0.640741 on 191 degrees of freedom
#   (731 observations deleted due to missingness)
# Multiple R-squared:  0.154383,    Adjusted R-squared:  0.132246
# F-statistic: 6.97411 on 5 and 191 DF,  p-value: 5.23897e-06

As expected since most of the data overlaps with the previous LLLT analysis, the LLLT variable correlates strongly; the individual magnesium variables may look a little more questionable but were justified in the magnesium citrate analysis. The Noopept result looks a little surprising - almost zero effect? Let’s split by dose (which was the point of the whole rigmarole of changing dose levels):

l2 <- lm(MP ~ as.factor(Noopept) +
              LLLT +
              as.logical(Magnesium.citrate) + as.integer(Date) + as.logical(Magnesium.citrate):as.integer(Date),
         data=noopeptSecond)
summary(l2)
# Coefficients:
#                                                        Estimate   Std. Error  t value   Pr(>|t|)
# (Intercept)                                        27.044709119 14.677995235  1.84253 0.06697191
# as.factor(Noopept)10                                0.099920147  0.139287051  0.71737 0.47403711
# as.factor(Noopept)15                                0.526389063  0.297940313  1.76676 0.07889108
# as.factor(Noopept)20                                0.114943375  0.147994400  0.77667 0.43832733
# as.factor(Noopept)30                                0.019029776  0.125504996  0.15163 0.87964479
# LLLTTRUE                                            0.329976497  0.096071943  3.43468 0.00072993
# as.logical(Magnesium.citrate)TRUE                  25.615810606 20.397271406  1.25584 0.21073068
# as.integer(Date)                                   -0.001488184  0.000913563 -1.62899 0.10499001
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.001610059  0.001269219 -1.26854 0.20617256
#
# Residual standard error: 0.639823 on 188 degrees of freedom
#   (731 observations deleted due to missingness)
# Multiple R-squared:  0.170047,    Adjusted R-squared:  0.13473
# F-statistic: 4.81487 on 8 and 188 DF,  p-value: 2.08804e-05

This looks interesting: the Noopept effect is positive for all the dose levels, but it looks like a U-curve - low at 10mg, high at 15mg, lower at 20mg, and even lower at 30mg 48mg and 60mg aren’t estimated because they are hit by the missingness problem: the magnesium citrate variable is unavailable for the days the higher doses were taken on, and so their days are omitted and those levels of the factor are not estimated. One way to fix this is to drop magnesium from the model entirely, at the cost of fitting the data much more poorly and losing a lot of R²:

l3 <- lm(MP ~ as.factor(Noopept) + LLLT, data=noopeptSecond)
summary(l3)
# Coefficients:
#                        Estimate Std. Error  t value   Pr(>|t|)
# (Intercept)           3.0564075  0.0578283 52.85318 < 2.22e-16
# as.factor(Noopept)10  0.1079878  0.1255354  0.86022 0.39031118
# as.factor(Noopept)15  0.1835389  0.2848069  0.64443 0.51975512
# as.factor(Noopept)20  0.1314225  0.1301826  1.00952 0.31348347
# as.factor(Noopept)30  0.0125616  0.1091561  0.11508 0.90845401
# as.factor(Noopept)48  0.2302323  0.2050326  1.12291 0.26231647
# as.factor(Noopept)60 -0.1714377  0.1794377 -0.95542 0.34008626
# LLLTTRUE              0.2801608  0.0829625  3.37696 0.00082304
#
# Residual standard error: 0.685953 on 321 degrees of freedom
#   (599 observations deleted due to missingness)
# Multiple R-squared:  0.0468695,   Adjusted R-squared:  0.0260848
# F-statistic: 2.25499 on 7 and 321 DF,  p-value: 0.0297924

This doesn’t fit the U-curve so well: while 60mg is substantially negative as one would extrapolate from 30mg being ~0, 48mg is actually better than 15mg. But we bought the estimates of 48mg/60mg at a steep price - we ignore the influence of magnesium which we know influences the data a great deal. And the higher doses were added towards the end, so may be influenced by the magnesium starting/stopping. Another fix for the missingness is to “impute” the missing data. In this case, we might argue that the placebo days of the magnesium experiment were identical to taking no magnesium at all and so we can classify each NA as a placebo day, and rerun the desired analysis:

noopeptImputed <- noopeptSecond
noopeptImputed[is.na(noopeptImputed$Magnesium.citrate),]$Magnesium.citrate <- 0
li <- lm(MP ~ as.factor(Noopept) +
              LLLT +
              as.logical(Magnesium.citrate) + as.integer(Date) + as.logical(Magnesium.citrate):as.integer(Date),
          data=noopeptImputed)
summary(li)
# Coefficients:
#                                                        Estimate   Std. Error  t value  Pr(>|t|)
# (Intercept)                                        10.430818153  8.189365582  1.27370 0.2036989
# as.factor(Noopept)10                                0.049595514  0.122841008  0.40374 0.6866772
# as.factor(Noopept)15                                0.405925320  0.281291053  1.44308 0.1499824
# as.factor(Noopept)20                                0.088343999  0.127014107  0.69554 0.4872219
# as.factor(Noopept)30                                0.029464990  0.106375169  0.27699 0.7819668
# as.factor(Noopept)48                                0.190340419  0.207736878  0.91626 0.3602263
# as.factor(Noopept)60                               -0.210638501  0.184357630 -1.14255 0.2540834
# LLLTTRUE                                            0.286295998  0.081098102  3.53024 0.0004765
# as.logical(Magnesium.citrate)TRUE                  42.273941799 16.288481089  2.59533 0.0098882
# as.integer(Date)                                   -0.000451814  0.000507568 -0.89015 0.3740561
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.002647546  0.001012691 -2.61437 0.0093648
#
# Residual standard error: 0.666405 on 318 degrees of freedom
#   (599 observations deleted due to missingness)
# Multiple R-squared:  0.108827,    Adjusted R-squared:  0.0808031
# F-statistic: 3.88332 on 10 and 318 DF,  p-value: 5.4512e-05

The 48mg/60mg coefficients shift downwards as expected. If we plot the coefficients with arm’s coefplot(), and one squints, the confidence intervals/point-values for Noopept look sort of consistent with a U-curve. What if we switch to a quadratic term to try to turn the Noopept values into a curve?

li2 <- lm(MP ~ Noopept + I(Noopept^2) +
               LLLT +
               as.logical(Magnesium.citrate) + as.integer(Date) + as.logical(Magnesium.citrate):as.integer(Date),
          data=noopeptImputed)
summary(li2)
# Coefficients:
#                                                        Estimate   Std. Error  t value   Pr(>|t|)
# (Intercept)                                         9.172594278  8.112803113  1.13063 0.25905147
# Noopept                                             0.008079500  0.006074315  1.33011 0.18442378
# I(Noopept^2)                                       -0.000178179  0.000122736 -1.45172 0.14755366
# LLLTTRUE                                            0.284419402  0.080959896  3.51309 0.00050627
# as.logical(Magnesium.citrate)TRUE                  41.589054331 16.141539488  2.57652 0.01042501
# as.integer(Date)                                   -0.000373812  0.000502850 -0.74339 0.45778931
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.002604384  0.001003433 -2.59547 0.00987860
#
# Residual standard error: 0.665408 on 322 degrees of freedom
#   (599 observations deleted due to missingness)
# Multiple R-squared:  0.100316,    Adjusted R-squared:  0.0835521
# F-statistic: 5.98394 on 6 and 322 DF,  p-value: 6.02357e-06

Looks better, but I’m not sure how well it fits. The quadratic $y=0.0080795x+-0.000178179x^2$ has its maximum around 40mg, though, which seems suspiciously high; it seems that in order to fit the negative estimate for 60mg, the ‘top’ of the curve gets pulled over to 48mg since it’s almost as big as 15mg. I don’t find that entirely plausible.

A fancier method of imputation would be “multiple imputation” using, for example, the R library mice (“Multivariate Imputation by Chained Equations”) (guide), which will try to impute all missing values in a way which mimicks the internal structure of the data and provide several ‘possible’ datasets to give us an idea of what the underlying data might have looked like, so we can see how our estimates improve with no missingness & how much of the estimate is now due to the imputation:

library(mice)
## work around apparent error in MICE: can't handle Dates type
## even though no missing-values in that column...?
noopeptSecond$Date <- as.integer(noopeptSecond$Date)
nimp <- mice(noopeptSecond, m=200, maxit=200)
li3 <- with(nimp, lm(MP ~ Noopept + I(Noopept^2) +
                          LLLT +
                          as.logical(Magnesium.citrate) + as.integer(Date) +
                            as.logical(Magnesium.citrate):as.integer(Date)))
round(summary(pool(li3)), 4)
#                                                        est     se       t       df Pr(>|t|)
# (Intercept)                                        -8.3369 3.2520 -2.5636 296.1619   0.0109
# Noopept                                             0.0073 0.0057  1.2790 521.5756   0.2015
# I(Noopept^2)                                       -0.0001 0.0001 -1.2808 583.2136   0.2008
# LLLT                                                0.3069 0.0910  3.3737 168.4541   0.0009
# as.logical(Magnesium.citrate)TRUE                   7.0763 3.9584  1.7877 298.7476   0.0748
# as.integer(Date)                                    0.0007 0.0002  3.4911 299.6728   0.0006
# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.0005 0.0002 -1.8119 300.7040   0.0710
#                                                       lo 95   hi 95 nmis    fmi lambda
# (Intercept)                                        -14.7369 -1.9368   NA 0.4974 0.4940
# Noopept                                             -0.0039  0.0185  457 0.2852 0.2824
# I(Noopept^2)                                        -0.0004  0.0001   NA 0.2411 0.2385
# LLLT                                                 0.1273  0.4864  599 0.6954 0.6918
# as.logical(Magnesium.citrate)TRUE                   -0.7135 14.8662   NA 0.4942 0.4908
# as.integer(Date)                                     0.0003  0.0011   NA 0.4930 0.4897
# as.logical(Magnesium.citrate)TRUE:as.integer(Date)  -0.0009  0.0000   NA 0.4918 0.4884

The coefficients & p-values agree, so it seems that it doesn’t make too much difference how we deal with missingness.

Finally, we can see if some weak priors/regularization changes the picture much by using a Bayesian regression instead:

library(arm)
bl1 <- bayesglm(MP ~ as.factor(Noopept) +
              LLLT +
              as.logical(Magnesium.citrate) + as.integer(Date) + as.logical(Magnesium.citrate):as.integer(Date),
          data=noopeptImputed)
display(bl1)
#                                                    coef.est coef.se
# (Intercept)                                        20.86     7.18
# as.factor(Noopept)10                                0.06     0.12
# as.factor(Noopept)15                                0.32     0.28
# as.factor(Noopept)20                                0.10     0.13
# as.factor(Noopept)30                                0.04     0.11
# as.factor(Noopept)48                                0.26     0.20
# as.factor(Noopept)60                               -0.13     0.18
# LLLTTRUE                                            0.27     0.08
# as.logical(Magnesium.citrate)TRUE                   0.28     1.33
# as.integer(Date)                                    0.00     0.00
# as.logical(Magnesium.citrate)TRUE:as.integer(Date)  0.00     0.00
# ---
# n = 329, k = 11
# residual deviance = 144.2, null deviance = 158.5 (difference = 14.3)
# overdispersion parameter = 0.5
# residual sd is sqrt(overdispersion) = 0.67
coefplot(bl1)

simulates <- as.data.frame(coef(sim(bl1, n.sims=100000)))
sapply(simulates[1:11], function(c) { quantile(c, c(.025, .975)) } )
#       (Intercept) as.factor(Noopept)10 as.factor(Noopept)15 as.factor(Noopept)20
# 2.5%   6.80794518         -0.179116006         -0.218679929         -0.151787205
# 97.5% 34.85995773          0.304713370          0.865894125          0.348912986
#       as.factor(Noopept)30 as.factor(Noopept)48 as.factor(Noopept)60    LLLTTRUE
# 2.5%          -0.174273139         -0.143056371         -0.490166499 0.114146706
# 97.5%          0.247145243          0.660125966          0.221157470 0.433830363
#       as.logical(Magnesium.citrate)TRUE as.integer(Date)
# 2.5%                        -2.29986917  -0.001966335149
# 97.5%                        2.86557048  -0.000227816111
#       as.logical(Magnesium.citrate)TRUE:as.integer(Date)
# 2.5%                                     -0.000197411805
# 97.5%                                     0.000124153915

The 95% credible intervals emphasize that while the mean estimates of the posterior for the Noopept parameters are positive, there’s substantial uncertainty after updating on the data, and the effects are small.

Should I run another followup experiment? No; the implied effect is so small a confirmatory experiment would have to run a miserably long time, it seems:

library(boot)
library(rms)
newNoopeptPower <- function(dt, indices) {
    d <- dt[sample(nrow(dt), n, replace=TRUE), ] # new dataset, possibly larger than the original
    lmodel <- lm(MP ~ Noopept + I(Noopept^2) +
               LLLT +
               as.logical(Magnesium.citrate) + as.integer(Date) + as.logical(Magnesium.citrate):as.integer(Date),
          data=d)
    return(anova(lmodel)[1:2,][5]$`Pr(>F)`)
}
alpha <- 0.05
for (n in seq(from = 100, to = 3000, by = 200)) {
   bs <- boot(data=noopeptImputed, statistic=newNoopeptPower, R=10000, parallel="multicore", ncpus=4)
   print(c(n, sum(bs$t<=alpha)/length(bs$t)))
}
# [1] 100.0000   0.0817
# [1] 300.0000   0.1145
# [1] 500.00000   0.15175
# [1] 700.00000   0.17825
# [1] 900.0000   0.2132
# [1] 1100.0000    0.2401
# [1] 1300.00000    0.26345
# [1] 1500.00000    0.28595
# [1] 1700.0000    0.3146
# [1] 1900.00000    0.33695
# [1] 2100.0000    0.3513
# [1] 2300.00000    0.37485
# [1] 2500.00000    0.39065
# [1] 2700.0000    0.4068
# [1] 2900.0000    0.4238

(I am not running an blind random self-experiment for 8 years just to get barely 40% power.)

Conclusion

So on net, I think there may be an effect but it’s small and I don’t know whether the optimal dose would be lower (~10mg) or much higher (~40mg). I don’t find this a particularly good reason to continue taking Noopept: it seems to either not be helpful in a noticeable way or to be redundant with the piracetam.