A New Medical Blog
Not knowing the gender attached to the name of its author, Ming-Chih Kao, I've made, in the interest of readability, the regrettably sexist assumption that he is a he. Kao is a 3rd year medical student at university of Michigan and his blog appears to have begun last month so it's quite new.
I doubt that this will be of general relevance throughout the blogosphere but for those of us involved in study design and methodology, it can be quite interesting. Kao is a newly minted Ph.D in biostatistics from Harvard and has posted some provocative thoughts on the medical literature often from a statistical perspective.
For example, this post caught my attention.
It deals with the common practice of discontinuing randomized clinical trials after statistical significance is achieved. For example, assume a study is analyzing the outcome of a disease following treatment with drug A vs. Drug B over a period of time. What is generally done is to analyze the data repeatedly as the study progresses.
If drug A shows statistically significant superiority at any time during the analysis, the study would be stopped. The idea is that the study objective has already been achieved and that to continue would subject the patients in the drug B arm of the trial to the deleterious effects of being treated with an inferior drug. This would be unethical.
Kao, however, points to an article in JAMA (Journal of the AMA) that demonstrates some pitfalls in this approach that I doubt most people would have thought of.
The argument is subtle but the gist of the concept is this: there is a statistical dispersion of expected results each time you re-analyze data prospectively (with new data points). If one simply stops the study when statistical significance is achieved without taking into account this predictable dispersion, results can be skewed to large treatment effects that would have perhaps been markedly smaller (or even been reversed) had the study been permitted to continue to completion.
Kao concludes with the apropos observation that "A watched pot never boils. Or not."
It's pretty intriguing stuff and challenges some basic assumptions many of us have about such an approach. Kao's blog is filled with such postings. I look forward to his reflections on the medical literature from the vantage point of his statistical background.
Additionally, count on me to correct the error of my ways if I discover that he is in fact a she!