It’s time for statisticians to stand up and speak. This is a time where most scientific papers are “probably wrong,” and many of the reasons listed are statistical in nature. A recent paper in Nature Neuroscience noted a major statistical error in a disturbingly large number of papers. And a recent interview with Deborah Zarin, director of ClinicalTrials.gov, in Science revealed the very disturbing fact that many primary investigators and study statisticians did not understand their trial designs and the conclusions that can be drawn from them.
Recent focus on handling these problems have primary been concerned with financial conflicts of interest. Indeed, disclosure of financial conflicts of interest has only improved reporting of results. However, there are other sources of error that we have to consider.
A statistician responsible for a study has to be able to explain a study design and state what conclusions can be drawn from that design. I would prefer that we dig into that problem a little deeper and determine why this is occurring (and fix it!). I have a few hypotheses:
- We are putting junior statisticians in positions of responsibility before they are experienced enough
- Our emphasis on classical statistics fills a lot of our education, but is insufficient for current clinical trial needs involving adaptive trials, modern dose-finding, or comparison of interactions
- The demand for statistical services is so high, and the supply so low, that statisticians are spread out too thin and simply don’t have the time to put in the sophisticated thought required for these studies
- Statisticians feel hamstrung by the need to explain everything to their non-statistical colleagues and lack the common language, time, or concentration ability to do so effectively
I’ve certainly encountered all of these different situations.