Sample size by simulation for clinical trials with survival outcomes: The simsam package in action

The simsam package, released earlier this year, allows versatile sample-size calculation (calculating sample size required to achieve given statistical power) using simulation for any method of analysis under any statistical model that can be programmed in Stata. Simulation is particularly helpful in situations where formulae for sample size are approximate or unavailable. The usefulness of the simsam package will depend in part, however, on the quality and variety of simsam applications developed by Stata users. I will discuss simsam applications for clinical trials with time-to-event or survival outcomes. Here sample size formulas are the subject of ongoing research (available methods for cluster-randomized trials with variable cluster size, for example, are only approximate). Using such examples, I will illustrate general simsam programming considerations such as dealing with analyses that fail, and the advantages of modular programming. Simulation forces us to think carefully about trial definitions—for example, whether recruitment ends after a fixed time or a fixed number of recruits—and to relate the sample-size calculation to a detailed or contingent analysis plan. Such careful attention to detail may be lost in a formulaic approach to sample-size calculation. Simulation also allows us to check for bias in a test as well as power. But simulation is not without problems: while a rare (say, one in a million) failure of an analysis in Stata may not worry the pragmatic statistician, a simsam application must anticipate this failure or risk stumbling on it in the course of many simulations.