Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits.
Here are a couple new preprints.
A proposed method for limiting the size of runs in a response-adaptive clinical trial.
Skeptical and optimistic robust priors for clinical trials.
Joint work with Jairo Fúquene and Luis Pericchi from University of Puerto Rico.
Back in March I wrote a blog post asking whether gaining weight makes you taller. Weight and height are clearly associated, and from that data alone one might speculate that gaining weight could make you taller. Of course causation is in the other direction: becoming taller generally makes you gain weight.
In the 1980’s, cardiologists discovered that patients with irregular heart beats for the first 12 days following a heart attack were much more likely to die. Antiarrythmic drugs became standard therapy. But in the next decade cardiologist discovered this was a bad idea. According to Philip Devereaux, “The trial didn’t just show that the drugs weren’t saving lives, it showed they were actually killing people.”
David Freedman relates the story above in his book Wrong. Freedman says
In fact, notes Devereaux, the drugs killed more Americans than the Vietnam War did—roughly an average of forty thousand a year died from the drugs in the United States alone.
Cardiologists had good reason to suspect that antiarrythmic drugs would save lives. In retrospect, it may be that heart-attack patients with poor prognosis have arrhythmia rather than arrhythmia causing poor prognosis. Or it may be that the association is more complicated than either explanation.
Related: Adaptive clinical trial design
How well can you predict height based on genetic markers?
A 2009 study came up with a technique for predicting the height of a person based on looking at the 54 genes found to be correlated with height in 5,748 people — and discovered the results were one-tenth as accurate as the 125–year-old technique of averaging the heights of both parents and adjusting for sex.
The quote above is from Wrong: Why experts keep failing us — and how to know when not to trust them by David Freedman.
The article Freedman quotes is Predicting human height by Victorian and genomic methods. The “Victorian” method is the method suggested by Sir Francis Galton of averaging parents’ heights. The article’s abstract opines
For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton’s method will long stay unsurpassed, in terms of both discriminative accuracy and costs.
Jon Udell’s latest Interviews with Innovators podcast features Randall Julian of Indigo BioSystems. I found this episode particularly interesting because it deals with issues I have some experience with.
The problems in managing biological data begin with how to store the raw experiment data. As Julian says
… without buying into all the hype around semantic web and so on, you would argue that a flexible schema makes more sense in a knowledge gathering or knowledge generation context than a fixed schema does.
So you need something less rigid than a relational database and something with more structure than a set of Excel spreadsheets. That’s not easy, and I don’t know whether anyone has come up with an optimal solution yet. Julian said that he has seen many attempts to put vast amounts of biological data into a rigid relational database schema but hasn’t seen this approach succeed yet. My experience has been similar.
Representing raw experimental data isn’t enough. In fact, that’s the easy part. As Jon Udell comments during the interview
It’s easy to represent data. It’s hard to represent the experiment.
That is, the data must come with ample context to make sense of the data. Julian comments that without this context, the data may as well be a list of zip codes. And not only must you capture experimental context, you must describe the analysis done to the data. (See, for example, this post about researchers making up their own rules of probability.)
Julian comments on how electronic data management is not nearly as common as someone unfamiliar with medical informatics might expect.
So right now maybe 50% of the clinical trials in the world are done using electronic data capture technology. … that’s the thing that maybe people don’t understand about health care and the life sciences in general is that there is still a huge amount of paper out there.
Part of the reason for so much paper goes back to the belief that one must choose between highly normalized relational data stores and unstructured files. Given a choice between inflexible bureaucracy and chaos, many people choose chaos. It may work about as well, and it’s much cheaper to implement. I’ve seen both extremes. I’ve also been part of a project using a flexible but structured approach that worked quite well.
I recently ran across this quote from Mithat Gönen of Memorial Sloan-Kettering Cancer Center:
While there are certainly some at other centers, the bulk of applied Bayesian clinical trial design in this country is largely confined to a single zip code.
from “Bayesian clinical trials: no more excuses,” Clinical Trials 2009; 6; 203.
The zip code Gönen alludes to is 77030, the zip code of M. D. Anderson Cancer Center. I can’t say how much activity there is elsewhere, but certainly we design and conduct a lot of Bayesian clinical trials at MDACC.
More clinical trial posts
Keith Baggerly and Kevin Coombes just wrote a paper about the analysis errors they commonly see in bioinformatics articles. From the abstract:
One theme that emerges is that the most common errors are simple (e.g. row or column offsets); conversely, it is our experience that the most simple errors are common.
The full title of the article by Keith Baggerly and Kevin Coombes is “Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology.” The article will appear in the next issue of Annals of Applied Statistics and is available here. The key phrase in the title is forensic bioinformatics: reverse engineering statistical analysis of bioinformatics data. The authors give five case studies of data analyses that cannot be reproduced and infer what analysis actually was carried out.
One of the more egregious errors came from the creative application of probability. One paper uses innovative probability results such as
P(ABCD) = P(A) + P(B) + P(C) + P(D) – P(A) P(B) P(C) P(D)
P(AB) = max( P(A), P(B) ).
Baggerly and Coombes were remarkably understated in their criticism: “None of these rules are standard.” In less diplomatic language, the rules are wrong.
To be fair, Baggerly and Coombes point out
These rules are not explicitly stated in the methods; we inferred them either from formulae embedded in Excel files … or from exploratory data analysis …
So, the authors didn’t state false theorems; they just used them. And nobody would have noticed if Baggerly and Coombes had not tried to reproduce their results.
More posts on reproducible research
Here are some of my favorite posts from the Reproducible Ideas blog.
Three reasons to distrust microarray results
Provenance in art and science
Forensic bioinformatics (continued)
Preserving (the memory of) documents
Programming is understanding
Musical chairs and reproducibility drills
Taking your code out for a walk
The most popular and most controversial was the first in the list, reasons to distrust microarray results.
The emphasis shifts from science to software development as you go down the list, though science and software are intertwined throughout the posts.
[Update: Reproducible Ideas has gone away.]
Before I started working for a cancer center, I was not aware of the tension between science and medicine. Popular perception is that the two go together hand and glove, but that’s not always true.
Physicians are trained to use their subjective judgment and to be decisive. And for good reason: making a fairly good decision quickly is often better than making the best decision eventually. But scientists must be tentative, withhold judgment, and follow protocols.
Sometimes physician-scientists can reconcile their two roles, but sometimes they have to choose to wear one hat or the other at different times.
The physician-scientist tension is just one facet of the constant tension between treating each patient effectively and learning how to treat future patients more effectively. Sometimes the interests of current patients and future patients coincide completely, but not always.
This ethical tension is part of what makes biostatistics a separate field of statistics. In manufacturing, for example, you don’t need to balance the interests of current light bulbs and future light bulbs. If you need to destroy 1,000 light bulbs to find out how to make better bulbs in the future, no big deal. But different rules apply when experimenting on people. Clinical trials will often use statistical designs that sacrifice some statistical power in order to protect the people participating in the trial. Ethical constraints make biostatistics interesting.
Hardly anyone cares about statistics directly. People more often care about decisions they need to make with the help of statistics. This suggests that the statistics and decision-making process should be explicitly integrated. The name for this integrated approach is “decision theory.” Problems in decision theory are set up with the goal of maximizing “utility,” the benefit you expect to get from a decision. Equivalently, problems are set up to minimize expected cost. Cost may be a literal monetary cost, but it could be some other measure of something you want to avoid.
I was at a conference this morning where David Draper gave an excellent talk entitled Bayesian Decision Theory in Biostatistics: the Utility of Utility. Draper presented an example of selecting variables for a statistical model. But instead of just selecting the most important variables in a purely statistical sense, he factored in the cost of collecting each variable. So if two variables make nearly equal contributions to a model, for example, the procedure would give preference to the variable that is cheaper to collect. In short, Draper recommended a cost-benefit analysis rather than the typical (statistical) benefit-only analysis. Very reasonable.
Why don’t people always take this approach? One reason is that it’s hard to assign utilities to outcomes. Dollar costs are often easy to account for, but it can be much harder to assign values to benefits. For example, you have to ask “Benefit for whom?” In a medical context, do you want to maximize the benefit to patients? Doctors? Insurance companies? Tax payers? Regulators? Statisticians? If you want to maximize some combination of these factors, how do you weight the interests of the various parties?
Assigning utilities is hard work, and you can never make everyone happy. No matter how good of a job you do, someone will criticize you. Nearly everyone agrees in the abstract that considering utilities is the way to go, but in practice it is hardly ever done. Anyone who proposes a way to quantify utility is immediately shot down by people who have a better idea. The net result is that rather than using a reasonable but imperfect idea of utility, no utility is used at all. Or rather no explicit definition of utility is used. There is usually some implicit idea of utility, chosen for mathematical convenience, and that one wins by default. In general, people much prefer to leave utilities implicit.
In the Q&A after his talk, Draper said something to the effect that the status quo persists for a very good reason: thinking is hard work, and it opens you up to criticism.