N=1: Hidden Variables and Superstition

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research

I.

Our psychology is focused on behavior. We focus on behavior because we want to figure out what actions we can take to influence the world around us. But a focus on our actions can also make us superstitious. 

The classic example is from a BF Skinner study, where he put a bunch of pigeons in a box and dropped in food at random intervals. Instead of realizing that the food drops were random, the pigeons assumed that they were somehow responsible and tried to figure out what they had done to make the food appear. 

Whatever they were doing at the time the food dropped, they tried again. A pigeon who had just turned counterclockwise when the food arrived would turn counterclockwise again and again. When more food eventually did arrive, the counterclockwise-turning was validated. “The experiment might be said to demonstrate a sort of superstition,” wrote Skinner. “The bird behaves as if there were a causal relation between its behavior and the presentation of food, although such a relation is lacking.” [1]

Compare this to a rat confronted with a set of buttons, trying to figure out which of the buttons give food and which give painful electric shocks. Unlike the pigeons, the rat is faced with a deterministic system where her actions lead directly to reward and punishment, so her focus on behavior is justified and leads to a correct understanding of the system. The pigeon is faced with a random system where his actions have nothing to do with the arrival of food, so his focus on behavior is pointless and leads only to superstition and confusion.

so cute though!

II.

We worry this is a common problem in chronic illness. Let’s say that Mary develops chronic fatigue syndrome (CFS). She is proactive and wants to solve the problem, so she comes up with a plan of 26 different treatments, which we’ll call A, B, C, D, and so on. Maybe A is “cut out dairy”, B is “walk 20 minutes every day”, etc. but the specific plans don’t really matter. She starts implementing each plan for two weeks, first plan A, then plan B, etc. 

But Mary is working from the wrong assumption. She thinks her chronic fatigue comes from something she’s doing or not doing. In short, she thinks it comes from her behavior. This is a common assumption because our psychology is focused on behavior — we look for things we are doing right or doing wrong. But what really happened is that last month she bought a bag of rice that was grown in a field that was contaminated with cadmium, and developed low-level cadmium poisoning, which is entirely responsible for her chronic fatigue. Cutting out dairy or walking to the corner store won’t do a thing, because the cadmium is the only cause of her illness. None of the interventions she has planned will help. 

But the cadmium is slowly being cleared from her system by natural means at the same time as she works her way through the 26 treatments. What happens is this: Mary reaches treatment L (“take omega-3 supplements”) just as the cadmium in her system drops below critical levels, and Mary is immediately “cured”. 

Since her symptoms stop almost immediately after starting treatment L, Mary assumes that the omega-3 supplements are what cured her, and continues taking them indefinitely. In reality, the omega-3 supplements do nothing for her — as long as her cadmium levels are low, she doesn’t have CFS, and if she ever gets exposed to high enough levels of cadmium again, her chronic fatigue will come right back.

III. 

What Mary should do is she should run a self-experiment with the omega-3 supplements. She should randomly assign some weeks to be on omega-3 supplementation, and some weeks to be off. If she did this, she would quickly find that the omega-3 makes no difference to her chronic fatigue.

It’s understandable why she doesn’t try this — she is worried that if she stops taking the omega-3, her chronic fatigue will come back, and she doesn’t want to risk it. Also, we suspect she wants a world that makes mechanical sense (“I just needed to take more omega-3”) rather than a world where she randomly gets sick and there’s nothing she can do to stop it. It’s hard to blame her for that.

This is how the focus falls on behavior and misses hidden variables. By “behavior”, we mean actions that are directly under people’s control. Eating more or less of something, getting up earlier or later, trying more or different kinds of exercise, and so on. By “hidden variables” we mean essentially any variable you wouldn’t normally think of, especially one not connected to your actions. For example, heavy metals in your drinking water, additives in your food, viruses you contracted from your friends, air pollution from forest fires hundreds of miles away, mold in your ceiling, or things you’re exposed to at work. 

Most of these hidden variables can be influenced by your actions, but they’re not the kinds of behaviors that come to mind. You can always quit your job, but for most people, that doesn’t come to mind as a possible treatment for their illness. You can cut out spinach or dairy, because “eat less dairy” is psychologically simple — but “consume less sulfites” isn’t a clear action for most people because “foods with sulfites” isn’t a category to most people. They may not always know which foods contain sulfites, and they may not know what sulfites are. 

Does this look like the face of mercy?

IV.

This is what chronic illness looks like for Mary as an individual. At the group level, things look somewhat different. 

If a chronic disease is caused by a hidden variable (like cadmium randomly being in some foods but not others), you should see something like this: People get sick for apparently no reason. They all try many different treatments, and most treatments don’t seem to work for anyone. Sometimes a treatment will seem to work for a bit, but then it will unexpectedly stop working. Whenever you feel like you start to get a firm grip on things, all the rules you learn go out the window. However, there are many individual stories of trying some new treatment and suddenly being cured. Unfortunately, the cures in all of these stories are entirely different treatments, and the cures that work for one person never seem to work for anyone else.

And this does sound like what we see in many chronic illnesses, which makes us suspect that some chronic illnesses are being caused by a hidden variable. It could be contamination in food, water, or air, like our hypothetical Mary’s experience with cadmium. But it could also be any other unexpected variable that doesn’t have to do with personal behavior. For example, it could be the result of a virus, or an allergy to something in your household, or a curse put on you by the local witch. When taken as a group, chronic illness communities look exactly how we would expect them to look if the illnesses were caused by some hidden variable, and that makes us suspect that they are caused by some hidden variable.

Naturally this makes us wonder if there is any way to figure out what these hidden variables might be, assuming you believe they exist. The fact that they are hidden does make it inherently tricky, but we have a couple of ideas, here they are.

TRY BIG ELIMINATIONS

Your chronic illness may be triggered by something in your environment (your home, work, local food, local water, etc.). To test this, you can change as much of your environment as possible all at the same time, for example by taking an extended trip to Nepal. 

If you start feeling better or your symptoms disappear, this strongly suggests that something in your home environment is causing your illness. If you don’t feel any better, it suggests that your symptoms 1) aren’t caused by your environment, 2) are caused by elements of your environment that you brought with you (e.g. your clothes, your shampoo), or 3) are caused by elements of your environment that are common to both your home and Nepal (car exhaust?).

Your chronic illness might also be triggered by something you eat. To test this, you can change as much of your diet as possible all at the same time, for example by trying the potato diet, where you eat essentially nothing but potatoes. The potato diet is good because potatoes are simple, contain no additives, and are more or less nutritionally complete. Many people can survive happily on nothing but potatoes, salt, water, and hot sauce for up to four weeks (we have good data on this!). 

If your symptoms disappear or get better, this strongly suggests that either you had some deficiency that the potato diet fixed, or something in your normal diet is causing your illness. If your illness is just as bad as ever, it suggests that either your symptoms aren’t caused by your diet, or are caused by elements of your diet that are also in the potato diet.

Neither of these approaches will tell you what is causing your illness, but both have the potential to narrow things down enormously. If you go to Finland for a month and your migraines stop three days in and don’t come back until you get home, that’s pretty clear evidence that something at home is causing your migraines. You don’t know if it’s your laundry detergent, your well water, or something at your job, but you can take steps to narrow it down further, and you can stop worrying about your diet so much. 

Similarly, if you try the potato diet for a month and your executive function issues disappear, you can stop worrying about fumes from your boiler and can try to figure out what part of your diet is giving you brainfog.

Even a null result is informative. If you go on the potato diet for a month and your migraines carry on as normal, that’s a pretty clear sign that it’s not something in your diet, and you should look elsewhere. 

Some people find this approach surprising, because scientific investigation usually involves isolating a small number of variables and putting them under tight control. This works fine when you have a small number of variables to start with, or you know which variables you’re interested in. But in the search for hidden variables, there are a nearly infinite number of things that could be the cause of your illness. We need a technique that lets us rule out lots of theories at once, so doing these big splits can be extremely productive.

There’s a classic genre of logic puzzles often called balance puzzles. In these puzzles you have several coins, one of which is lighter than all the others, and you have to use a balance scale to find the light coin in the smallest number of weighings. The way you solve these problems is by splitting the coins into groups and comparing the groups directly. If you split the coins into two groups and the group on the right weighs less than the group on the left, the light coin must be in that group.

spoilers

Consider a version of this puzzle where there is an illuminated lightbulb and a row of 1,000 switches. You want to find the switch that controls the lightbulb, but you don’t know which it is. You could go down the row of switches and try them one by one, but this would probably take you several hundred steps. On the other hand, if you have some kind of opportunity to flip a bunch of switches at once, that can narrow things down really quickly. 

Let’s say that half the switches are red and half are blue, and you can flip all the switches of a single color at once. If you flip all the red switches and the light goes off, then the master switch must be red. If you flip all the red switches and the light stays on, then the master switch must be blue. Either way, you now have only 500 switches to try. 

This is the same situation we’re in with chronic illness, except that there are something like 1,000,000 switches on the wall, and in some cases the lightbulb might be controlled by complicated interactions between multiple switches. It still makes sense to toggle big groups of switches all at once when you can, because that can narrow things down drastically.

One limitation of this approach is that it’s only really good at finding triggers. If you’re suffering from an iron deficiency, big eliminations probably won’t help find that.

The other limitation of this approach is that it’s not always clear how long you have to eliminate things for. Do you need to eliminate the mystery trigger for a week? A month? Longer? Ideally we could send you to Nepal for a year, or put you on the potato diet for a year, but in reality this won’t be practical for most people.

If Mary is getting poisoned by cadmium, and it takes two months to clear all the cadmium from her system, then going on a restrictive diet for only one month won’t help. But the problem is, she can’t know this in advance. How is she supposed to know about the clearance rate when she doesn’t even know what’s poisoning her?

So we’re stuck with an asymmetry. If one of these eliminations helps you, that narrows things down quite a bit. But if they don’t help you, then it’s more ambiguous. Maybe the half-life of whatever is making you sick is just too long. Still, it seems like it would be worth trying.

TRY SOME LIKELY VARIABLES AT RANDOM

Another possibility is to just try various things and see what works. This is grasping in the dark, but we can still do a lot to cover our bases.

For example, there are a finite number of vitamins. “Vitamin deficiency” is a plausible type of hidden variable, so you could just cycle through all the vitamins and see if any of them happen to be an immediate cure. It seems unlikely that you will get this kind of miracle result, but vitamins are pretty safe so the risk is very low, and you could at least check “vitamin deficiency” off your list.

Doesn’t happen very often, but you know, sometimes it does

Similarly, there are a finite number of elements. Some of them, like iron and potassium, are necessary for human health. You could try supplementing these and see if that treats your illness. Other elements, like lead and mercury, are known to be bad for your health. You could try getting blood and/or urine tests, or testing your local water supply, and see if you have higher exposure to any of these known toxins. 

Again these are all shots in the dark, but they’re all plausible variables that could be affecting your health. If it turns out your blood mercury levels are way higher than normal, that would be good to know.

You could also try to hit your illness with some generic treatments, basically anything where the name starts with the prefix “anti-”. If you can convince your doctor, you could maybe get them to put you on a broad-spectrum antibiotic (in case your chronic illness is bacterial), antiviral (in case your chronic illness is… etc.), antifungal, anti-inflammatory, or antihistamine. This is a little more risky, but there’s some chance your chronic illness might be fungal and this is one of the only ways you would ever find out. 

This broad-spectrum approach will generally be better for finding deficiencies, but in some cases it might also help identify triggers.

GET RID OF YOUR BLOOD

Haha, but no, seriously. Donating blood is easy, safe, and it’s a nice thing to do for your community. You might save a life. And if there’s something nasty building up in your blood, you might be able to get rid of some of it. There’s already some evidence that donating blood can reduce your serum PFAS levels. Maybe it can clear some other things from your system.

Again, this is a pretty blind approach. It probably won’t work for most people. It may not work for anyone at all. But if you donate blood and your symptoms immediately get better, that would be pretty interesting, right? 

V.

In the beginning, we’ll be taking shots in the dark. People will try dozens of things with little or no success. This will be quite frustrating.

But the hope is that eventually, we will start to get our bearings. If a couple people with chronic fatigue find that they have high levels of cadmium in their blood, then other people with chronic fatigue will want to check their cadmium levels before trying other interventions. Conversely, if a couple dozen people with chronic fatigue check for cadmium and find nothing, checking for cadmium should be moved lower down on the list for chronic fatigue. 

Over time, some people will get very skilled at organizing and interpreting this kind of research. And with enough people trying things and going over the data together, tricky bugs quickly become shallow

Depending on the success of this approach, you can even imagine this being somewhat formalized. Someone could make a centralized list of things to try or to have tested, and people could report what they had tried and whether it worked out. Tests that seem to be helpful could be moved up in the rankings so people could know to try them first; tests that don’t seem to help people could be moved down and left for the last ditch attempt.

Will this work at all? Who knows, but it seems like it’s worth trying. And at the very least, we may be able to rule out some hypotheses. After all, if we can establish that it’s none of the things on this list… then what the hell is it? 

Endnotes

[1]: 

“A pigeon is brought to a stable state of hunger by reducing it to 75 percent of its weight when well fed. It is put into an experimental cage for a few minutes each day. A food hopper attached to the cage may be swung into place so that the pigeon can eat from it. A solenoid and a timing relay hold the hopper in place for five sec. at each reinforcement.

If a clock is now arranged to present the food hopper at regular intervals with no reference whatsoever to the bird’s behavior, operant conditioning usually takes place. In six out of eight cases the resulting responses were so clearly defined that two observers could agree perfectly in counting instances. One bird was conditioned to turn counter-clockwise about the cage, making two or three turns between reinforcements. Another repeatedly thrust its head into one of the upper corners of the cage. A third developed a ‘tossing’ response, as if placing its head beneath an invisible bar and lifting it repeatedly. Two birds developed a pendulum motion of the head and body, in which the head was extended forward and swung from right to left with a sharp movement followed by a somewhat slower return. The body generally followed the movement and a few steps might be taken when it was extensive. Another bird was conditioned to make incomplete pecking or brushing movements directed toward but not touching the floor. None of these responses appeared in any noticeable strength during adaptation to the cage or until the food hopper was periodically presented. In the remaining two cases, conditioned responses were not clearly marked.

The conditioning process is usually obvious. The bird happens to be executing some response as the hopper appears; as a result it tends to repeat this response. If the interval before the next presentation is not so great that extinction takes place, a second ‘contingency’ is probable. This strengthens the response still further and subsequent reinforcement becomes more probable. It is true that some responses go unreinforced and some reinforcements appear when the response has not just been made, but the net result is the development of a considerable state of strength.”

N=1: Single-Subject Research

Previously in this series:
N=1: Introduction

History

Single-subject or single-case research designs date back to at least the 1980s — though as you can see from the sparse Wikipedia page, they haven’t gotten that much attention. While the idea of single-subject research is good, the execution tends to be crap. 

The simplest single-subject design is the “ABA” design, where you have a few control days (“A”), a few days on the treatment (“B”), and then you go back to the control (“A”). See for example this figure from the University of Connecticut:

In this case, we can see that “frequency of disruptions” (from a target student) is high in the first baseline A block, goes down for a while in the B block (“praise”), and goes back up when they switch back to A.

The ABA design is better than nothing. It’s better than a case study, because at least there’s some experimental control. But it just doesn’t give us all that much information. They have 15 days of data, sure, but only three phases. This is really more like a sample size of three (not that a sample size of 15 is all that much more compelling).

Some sources might recommend the more advanced ABAB approach…

…but ABAB isn’t all that convincing either. Is that a change in the periods of “positive attention”, or just a random walk? It’s pretty hard to tell.

The ABA approach is what Seth Roberts used to argue that his “butter mind” protocol was effective, i.e. that eating more butter made him smarter. Take a look at this ABA graph from one of his self-experiments:

Again, this is vague at best. Yes, it took him longer to do arithmetic problems on 30 g/day butter than on 60 g/day butter. But the change is not very distinct, the three periods clearly overlap, it’s not clear if the periods were randomly determined, etc. And most of all, the sample size is just not very large — we can do better than drawing strong conclusions from a mere three intervals.

You shouldn’t totally discount these simple designs. Starting here can be fine (see for example Allan Neuringer’s self-experiments). Simplicity is important, and if you’re doing early stage exploratory work, going with a study design that’s easy has obvious benefits. If you don’t see any difference with this simple approach, you can move on to something else. If you do see a difference in the ABA, and you want to demonstrate that difference in a more convincing way, then you can expand it into a true within-subjects study. 

There’s nothing wrong with Seth’s butter ABA — it’s just that it looks like the start of something, rather than a conclusion. If he wanted to really convince people, he should have spun that off into a longer self-experiment. 

Within-Subjects Approach

Emily is a woman in her late 20s who gets migraines on a regular basis. She usually gets hit with a migraine in the middle of the afternoon, and they happen almost every day. Emily has been tracking her migraines for a long time, and has determined that each day there’s an 80% chance that a migraine will descend on her at about 2:00 pm, ruining the rest of her afternoon.

Emily has recently noticed that if she takes 400 mg magnesium in the morning, it’s much less likely she’ll have a migraine that afternoon. This isn’t a sure thing — she still gets migraines on days when she takes the magnesium — but it seems like it’s less than 80%.

One way for Emily to get some support for this hypothesis would be for her to run a simple AB self-experiment. She could take no magnesium for two weeks, then take 400 mg magnesium every morning for two weeks, and see if it makes a difference for her migraines. If she gets migraines 80% of the time in weeks where she’s taking no magnesium and only 40% of the time in weeks where she’s taking 400 mg magnesium, that seems like evidence that the magnesium is helping. 

And it is evidence, but it’s on the weak side. Depending on how you slice it, the effective sample size here is two — just one fortnight with magnesium, and one fortnight without. You shouldn’t draw strong conclusions here for the same reason that you shouldn’t draw strong conclusions from a study with one person in the experimental group and one person in the control group. There’s just not that much evidence. 

With small sample sizes, there are too many alternative explanations, it’s too easy to fool yourself. Maybe she started taking magnesium in the springtime, and the increased daylight is the real reason her migraines improved. Or she just started a new job two months ago, and it took her two months to stop grinding her teeth, which happened to align with the switch over to magnesium. Or her detergent manufacturer switched to a new supplier for fragrances. Or maybe her mailman’s cat, which she’s allergic to, ran away last week. It could be almost anything.

To account for these problems, Emily can just go ahead and get a larger sample size. She can use a random number generator to randomly assign days to either take magnesium or not take magnesium, and then follow that random assignment.

With the addition of these two small steps, she can now use a normal within-subjects experimental approach. Let’s imagine she finds that there’s an 80% chance of developing a migraine on days when she takes no magnesium, and a 30% chance of developing a migraine on days when she takes 400 mg magnesium. She can demonstrate this difference to an arbitrary level of precision, just by running the trial for more days.

Emily’s data might look something like this. The data shown here is not quite enough to reach statistical significance (χ2 gives p = .051) but it’s looking pretty good for 30% chance of migraines with magnesium and 80% chance without.

Even if the difference is much more subtle — perhaps a 75% chance of a migraine with 400 mg magnesium and 80% without — with enough days, she can still show to an arbitrary level of confidence that the magnesium has the observed effect FOR HER, however small that effect might be. 

This wouldn’t provide any evidence that magnesium will work for anyone ELSE’s migraines. But even if it doesn’t generalize at all to anyone else, Emily can get as much evidence as she wants that it’s really doing something for her. And that still helps the community, because it shows that the treatment works at least sometimes, for some people. 

This is different from a traditional case study. Even though it’s looking at just one person, it uses experimental techniques. Compared to a traditional experiment, you sacrifice external validity (will this generalize to anyone other than this one person?) but you still get the same level of statistical rigor and you can still clearly infer causality. 

And with this design, you should be able to use standard within-subjects statistical approaches. A sample size of just one is unusual even for a within-subjects study, but not entirely unheard of.

This approach is under-used in the internet research community (though Scott Alexander did one here and Gwern did one with LSD). Lots of people are online sharing tips and tricks on things they think might help their reflux/migraines/IBS/heart palpitations/executive function/etc. This is good, but it’s hard to know which recommendations are solid and which are just random chance. 

If you run a within-subject self-experiment, you can do something incredible for your community. It may not help everyone, but you can demonstrate whether it works for you. Publish your null results too — if you suspect that caffeine triggers your reflux, but under close inspection the hypothesis falls apart, report that shit!

We should emphasize that N = 1 studies falsify a very specific kind of null hypothesis: that an intervention cannot work. If the intervention works for you, that just shows that the intervention can work.

It might not work for anyone else, and with N = 1 you have a much higher chance that one person will do something idiosyncratic that makes it look like the intervention was successful, when in fact it was the idiosyncratic thing doing all the work. For example, maybe on days you take magnesium, you always take it with a big glass of lemonade. It turns out the lemonade is what’s helping your symptoms, not the magnesium, and this wouldn’t be apparent from the data, because the lemonade and the magnesium are confounded.

If you want to go above and beyond, you can get a couple friends and do an even more compelling test with only a few people. As long as you all do multiple trials, your effective sample size from a statistical standpoint can still be arbitrarily large. With more people, we have greater certainty that there isn’t something weird confounded with the experimental variable (but never 100%). Every chronic illness subreddit should be generating research pods of 2-10 people, and testing the treatments they think are worth investigating. 

Limitations

Even with better randomization, however, these designs still have a lot of limitations.

For a start, they’re limited by the speed of your research cycle. For example, repeated-measures studies won’t work very well for studying obesity, because people tend to lose and gain weight pretty slowly. It may take months to lose and then regain weight, so it’s hard to study weight gain with this method. If you have to randomize periods of months, it will take you a full year to get a sample size of 12. In comparison, headaches would be easier to study, since they come and go daily or even hourly, and you could randomize your treatment on much shorter timescales.

Worse, for some treatments we don’t know what the appropriate timescales will be. Let’s return to Emily and her migraines. If magnesium works on the order of weeks rather than days, she will have to randomly assign weeks rather than days, which means it will take seven times longer to reach an equivalent sample size. But how can she know in advance whether to use periods of days or of weeks?

If a cure is too powerful, or has long-lasting effects, that actually makes it harder to study. If magnesium cures Emily’s migraines for a month, she’ll have to wait a month between randomization cycles, and it will take her years to get a decent sample size.

Similarly, this kind of protocol may not be able to detect more complicated relationships. If Emily’s body builds up a reservoir of extra magnesium over time, this may be difficult to model and might throw off the clarity of the experiment. Or if her body gets more aggressive about clearing the excess magnesium from her system, the magnesium will have less effect over time, and will have even less effect on trial runs where she has multiple magnesium blocks one after another. These designs have a lot to offer, but they’re not going to get us very far in the face of genuinely complex problems.

Another downside of this approach is that Emily has already found a treatment she likes. Probably she would like to take magnesium every day and get as few migraines as possible, but to do this within-subjects self-experiment, she has to try going off the magnesium multiple times over the course of several months, to make sure it really works. We think it’s often worth it to know for sure, but it isn’t easy to stop a treatment that seems to be working.

Finally, the big limitation is that you can only use this approach if you’ve already identified a treatment that you suspect might work for you. If you’re sick and you don’t have any leads, this method can’t help you figure out what to try. It’s only good for testing or confirming hypotheses — it can’t give you any new ones, can’t narrow down a list of cures or triggers out of the huge number of things that might possibly be making you sick.

N=1: Introduction

I.

The history of science is old, but most research methods are not. 

People have been doing research for thousands of years. But many of the methods we consider standard today — including questionnaires, blind and double-blind research, and the idea of control groups — were all invented after 1700. The first randomized controlled trial in medicine wasn’t published until 1948, and the term “evidence-based medicine” didn’t find its way into print until 1992

Until recently, all research methods looked more or less like this: A demonstration to the Royal Society by Waller’s pet bulldog ‘Jimmie’

Research methods are still very new, probably we can sit down and invent some more. 

This is good news, because right now there are many problems that we have no idea how to solve. One area of particular mystery is human health. Doctors can do a lot for you with surgery, vaccines, and antibiotics, but outside of these interventions there remain many ailments that totally stump the system. 

A weird part of the postmodern experience is that many people feel kinda bad all the time, even if they aren’t “sick”. If you go to the doctor and you’re like “I’m feeling kinda bad”, they don’t know how to help you.

Being “actually sick” doesn’t get you much further. If anything it’s worse. Lots of people have mystery chronic illnesses, but when you go to doctors with one of these problems, they mostly just shrug at you. 

II. 

Alistair Kitchen began having stomach pain. It started out small, but over time grew to “an intensity of pain I didn’t know my body was capable of producing, a literally blinding sensation that shut down every sense in my body except the sensations of my stomach.” He says:

So, four years of this. In the third year, after an endoscopy and a series of scans had cleared me for anything “serious”, the advice given to me was, essentially, this:

Look, some people just have trouble with their stomachs. When they have trouble and we don’t know what is causing it, we just call it IBS. So you have IBS. Watch out for foods that might trigger you, and good luck.

Or take for example the experience of Elisabeth von Nostrand

I had a lot of conversations like the following:

Me (over 20 pages of medical history and 30 minutes of conversation): I can’t digest protein or fiber, when I try it feels like something died inside me. 

Them: Oh that’s no good, you need to eat so much protein and vitamins

Me: Yes! Exactly! That’s why I made an appointment with you, an expensive doctor I had to drive very far to get to. I’m so excited you see the problem and for the solution you’re definitely about to propose.

Them: What if you took a slab of protein and chewed it and swallowed it. But like a lot of that.

Me: Then I’d feel like something died inside me, and would still fail to absorb the nutrients which is the actual thing we want me to get from food.

Them: I can’t help you if you’re not willing to help yourself.

It’s an uncomfortably common story.

Having faced this system, many people end up taking their health into their own hands. This makes a lot of sense and we fully endorse it. But most people have no more success on their own than they do with doctors (though at least they’re not being condescended to). 

It seems like the average outcome is that you end up living with your mystery illness (or even just your mystery sense-of-mild-feeling-bad-all-the-time) for years. It either never goes away, or randomly goes away some day for no apparent reason.

III. 

We suspect that people do about as well on their own as they do with doctors because *no one* knows how to study individual issues. This is because our civilization has done a good job developing population-level research techniques, but a crummy job so far coming up with individual-level research techniques.

Our society has devoted a lot of time to doing research on large groups. We’ve come up with lots of ways of running studies on large samples, and lots of ways of thinking about it. We’d bet that 99% of the studies you’ve ever read are studies on groups.

In comparison, doing research on individuals is a very understudied and (dare we say) cutting-edge form of research. Scientists mostly haven’t developed techniques for it, because almost by definition, it isn’t the kind of thing they study. 

Possibly this is because doctors and researchers are more interested in population-level issues. After all, they are usually tasked with solving public health crises, tasked with curing common diseases, things that might affect millions of people. But individuals care more about, well, individuals.

Possibly this is because we started by focusing on the most common illnesses and are only now getting around to the rare ones. Common illnesses are best studied by looking at large groups, so we developed those techniques first, and are only now running up against their limitations.

Possibly it is all a question of computational power. The history of statistics is tainted, because statistics was invented before computers, and was designed within the limits of what a person can reasonably calculate by hand. Even up to the 1990s, consumer machines would take weeks to crunch the kinds of models that today you can run in 15 minutes on your phone. But now we can do more, and maybe that means we can do new things, things that weren’t possible before.

In any case, it must be possible to come up with protocols for such a thing. 

“Jimmy with Electrodes”

Individual-level research comes with certain advantages. The problem with population-level techniques is that the same treatment will always work better for some people than for others. If you give two people the same drug, it might work great for one of them and not work at all for the other, and your statistical modeling needs to take that into account. Individual-level research doesn’t have to worry about that! You are just looking at one person.

A technical way of describing this is that individual-level research always has high internal validity — the research question is “does this treatment protocol work for this person” and you always get a straight answer to that question. This comes at the cost of external validity — you have basically no idea whether your findings will generalize to any other person. That’s an ok tradeoff, because you are already choosing to study an individual, and because population-level techniques have questionable external validity to begin with.

We may also be able to use population-level techniques to guide individual-level research, and individual-level techniques to guide population-level research; there may be many ways in which they are complementary.

Individual-level techniques won’t be limited to studying chronic illness — you could also use them, for example, to make healthy people feel amazing more often, which would be pretty cool. People who already feel amazing all the time, you’re on your own. 

But chronic illness is a good place to start, because these illnesses are a drain on the lives of millions of people who are motivated to figure out a treatment, and population-level medicine isn’t cutting it.

This is a problem we have been mulling over and that we will be writing about over the next couple months. To start with, here are some simple distinctions that seem like they might come in handy: 

Testing vs. Finding Variables

When studying an individual, there are two main situations.

One situation is where you think you know some variables that are involved with your illness, and you want to test them. For example, you may suspect that caffeine makes you feel nauseous. You want to confirm this hypothesis or rule it out. You might also want to demonstrate to a high degree of certainty that caffeine is really a trigger for you, so you can write about it on the internet and other people with random nausea can benefit from your example, possibly by trying it for themselves.

The other situation is where you have no idea what is causing your illness. When you have no clear leads, you want techniques that can help you find variables that might plausibly be involved. This is a much harder problem, but it’s also much more important, because many people are chronically sick and have no idea what is causing their illness. 

This sucks, and it’s very tricky because there are approximately infinity variables in the world. But probably we can do better than “try variables at random” (even if some level of luck is inevitably involved), and we should see if we can come up with techniques for this situation.

Triggers vs. Deficiencies

Sometimes a chronic illness is caused by getting too much of something, like an allergen. These are generally known as triggers. Sometimes a chronic illness is caused by not getting enough of something, like a vitamin. We call these deficiencies

This distinction seems like it might be helpful, because the techniques for finding triggers may be very different from the techniques for finding deficiencies. (More on this in future posts.)

We should also keep in mind that chronic illnesses can be more complicated than simply getting too much or too little of something. Some chronic illnesses aren’t caused by any external variable — there’s always the possibility that you have a brain tumor or something, in which case there may be no triggers or deficiencies involved.

Ruling In vs. Ruling Out

Sometimes we will be in the fortunate position where we can get a lot of evidence that X causes Y. If we can conclusively pin things down and show that dairy causes your chronic nausea, that’s great. Now you can keep yourself from feeling sick all the time, and that’s enormously valuable. 

But sometimes we won’t be able to get evidence in favor of anything — we will only be able to disprove things. It’s important to remember that this is valuable too. Maybe you suspect that your nausea might be caused by dairy, caffeine, alcohol, or fatty foods. If you go through them one by one and rule them all out — nope it’s not dairy, it’s not caffeine, not alcohol, not fatty foods — that’s still good to know. 

It will be disappointing that you continue to feel nauseous all the time and you don’t know why. It may feel like a step backwards, because you’ve ruled out all your best guesses. But it’s still enormous progress. Disproving a hypothesis is valuable, and at least now you’ll be able to enjoy your milkshakes, Irish coffees, and beer-battered onion rings without fear.

The best thing research can offer you is a cure, but the second-best thing it can offer is some peace of mind.