David Judkins

Robustness of ordinary least squares in randomized clinical trials.

There has been a series of occasional papers in this journal about semiparametric methods for robust covariate control in the analysis of clinical trials. These methods are fairly easy to apply on currently available computers, but standard software packages do not yet support these methods with easy option selections. Moreover, these methods can be difficult to explain to practitioners who have only a basic statistical education. There is also a somewhat neglected history demonstrating that ordinary least squares (OLS) is very robust to the types of outcome distribution features that have motivated the newer methods for robust covariate control. We review these two strands of literature and report on some new simulations that demonstrate the robustness of OLS to more extreme normality violations than previously explored. The new simulations involve two strongly leptokurtic outcomes: near-zero binary outcomes and zero-inflated gamma outcomes. Potential examples of such outcomes include, respectively, 5-year survival rates for stage IV cancer and healthcare claim amounts for rare conditions. We find that traditional OLS methods work very well down to very small sample sizes for such outcomes. Under some circumstances, OLS with robust standard errors work well with even smaller sample sizes. Given this literature review and our new simulations, we think that most researchers may comfortably continue using standard OLS software, preferably with the robust standard errors.

Panel discussion 2

Susan Ellenberg: Our four distinguished panelists are going to comment on what they knew before they came here, what they have heard since they have been here or what they anticipate other people are going to say later on. Our distinguished panelists are Bryan Luce, United BioSource Corporation; Brian Strom, the George Pepper Professor of Public Health and Preventive Medicine at the University of Pennsylvania’s Perelman School of Medicine; Jay Siegel, Chief Biotechnology Officer and Head of Global Regulatory Affairs for Pharmaceuticals at Johnson and Johnson; and Tom Fleming, Professor of Biostatistics at the University of Washington.