Clinical trials

Innovation II

Posted on by John

In 1601, an English sea captain did a controlled experiment to test whether lemon juice could prevent scurvy.  He had four ships, three control and one experimental.  The experimental group got three teaspoons of lemon juice a day while the control group received none. No one in the experimental group developed scurvy while 110 out of 278 in the control group died of scurvy. Nevertheless, citrus juice was not fully adopted to prevent scurvy until 1865.

Overwhelming evidence of superiority is not sufficient to drive innovation.

Source: Diffusion of Innovations

False positives for medical papers

Posted on by John

My previous two posts have been about false research conclusions and false positives in medical tests. The two are closely related.

With medical testing, the prevalence of the disease in the population at large matters greatly when deciding how much credibility to give a positive test result. Clinical studies are similar. The proportion of potential genuine improvements in the class of treatments being tested is an important factor in deciding how credible a conclusion is.

In medical tests and clinical studies,  we’re often given the opposite of what we want to know. We’re given the probability of the evidence given the conclusion, but we want to know the probability of the conclusion given the evidence. These two probabilities may be similar, or they may be very different.

The analogy between false positives in medical testing and false positives in clinical studies is helpful, because the former is easier to understand that the latter. But the problem of false conclusions in clinical studies is more complicated. For one thing, there is no publication bias in medical tests: patients get the results, whether positive or negative. In research, negative results are usually not published.

Most published research results are false

Posted on by John

John Ioannidis wrote an article in Chance magazine a couple years ago with the provocative title Why Most Published Research Findings are False.  [Update: Here’s a link to the PLoS article reprinted by Chance. And here are some notes on the details of the paper.] Are published results really that bad? If so, what’s going wrong?

Whether “most” published results are false depends on context, but a large percentage of published results are indeed false. Ioannidis published a report in JAMA looking at some of the most highly-cited studies from the most prestigious journals. Of the studies he considered, 32% were found to have either incorrect or exaggerated results. Of those studies with a 0.05 p-value, 74% were incorrect.

The underlying causes of the high false-positive rate are subtle, but one problem is the pervasive use of p-values as measures of evidence.

Folklore has it that a “p-value” is the probability that a study’s conclusion is wrong, and so a 0.05 p-value would mean the researcher should be 95 percent sure that the results are correct. In this case, folklore is absolutely wrong. And yet most journals accept a p-value of 0.05 or smaller as sufficient evidence.

Here’s an example that shows how p-values can be misleading. Suppose you have 1,000 totally ineffective drugs to test. About 1 out of every 20 trials will produce a p-value of 0.05 or smaller by chance, so about 50 trials out of the 1,000 will have a “significant” result, and only those studies will publish their results. The error rate in the lab was indeed 5%, but the error rate in the literature coming out of the lab is 100 percent!

The example above is exaggerated, but look at the JAMA study results again. In a sample of real medical experiments, 32% of those with “significant” results were wrong. And among those that just barely showed significance, 74% were wrong.

See Jim Berger’s criticisms of p-values for more technical depth.

Population drift

Posted on by John

The goal of a clinical trial is to determine what treatment will be most effective in a given population. What if the population changes while you’re conducting your trial? Say you’re treating patients with Drug X and Drug Y, and initially more patients were responding to X, but later more responded to Y. Maybe you’re just seeing random fluctuation, but maybe things really are changing and the rug is being pulled out from under your feet.

Advances in disease detection could cause a trial to enroll more patients with early stage disease as the trial proceeds. Changes in the standard of care could also make a difference. Patients often enroll in a clinical trial because standard treatments have been ineffective. If the standard of care changes during a trial, the early patients might be resistant to one therapy while later patients are resistant to another therapy. Often population drift is slow compared to the duration of a trial and doesn’t affect your conclusions, but that is not always the case.

My interest in population drift comes from adaptive randomization. In an adaptive randomized trial, the probability of assigning patients to a treatment goes up as evidence accumulates in favor of that treatment. The goal of such a trial design is to assign more patients to the more effective treatments. But what if patient response changes over time? Could your efforts to assign the better treatments more often backfire? A trial could assign more patients to what was the better treatment rather than what is now the better treatment.

On average, adaptively randomized trials do treat more patients effectively than do equally randomized trials. The report Power and bias in adaptive randomized clinical trials shows this is the case in a wide variety of circumstances, but it assumes constant response rates, i.e. it does not address population drift.

I did some simulations to see whether adaptive randomization could do more harm than good. I looked at more extreme population drift than one is likely to see in practice in order to exaggerate any negative effect. I looked at gradual changes and sudden changes. In all my simulations, the adaptive randomization design treated more patients effectively on average than the comparable equal randomization design. I wrote up my results in The Effect of Population Drift on Adaptively Randomized Trials.

Related: Adaptive clinical trial design

Irrelevant uncertainty

Posted on by John

Suppose I asked where you want to eat lunch. Then I told you I was about to flip a coin and asked again where you want to eat lunch. Would your answer change? Probably not, but sometimes the introduction of irrelevant uncertainty does change our behavior.

Here’s an example I’ve seen repeatedly. In adaptive clinical trials, a patient’s treatment is influenced by the data on all previous patients. It is often the case that a particular observation has no immediate impact. Suppose Mr. Smith’s outcome is unknown. We calculate what the treatment for the next patient will be if Mr. Smith responds well and what it will be if he does not. If both doses are the same, why wait to know his outcome before continuing? Some people accept this reasoning immediately, but others are quite resistant.

Not only may a patient’s outcome be irrelevant, the outcome of an entire clinical trial may be irrelevant. I heard of a conversation with a drug company where a consultant asked what the company would do if their trial were successful. He then asked what they would do if it were not successful. Both answers were the same. He then asked why do the trial at all, but his question fell on deaf ears.

While it is irrational to wait to resolve irrelevant uncertainty, it is a human tendency. For example, businesses may delay a decision on some action pending the outcome of a presidential election, even if they would take the same action regardless which candidate won. I see how silly this is when other people do it, but it’s not too hard for me to think of analogous situations where I act the same way.