Sample size methods for evaluation of predictive biomarkers

Treatment selection biomarkers are those that can be useful in guiding choice of therapy. Just as new therapies require evaluation in appropriately designed clinical trials to determine their benefit, therapy selection biomarkers require evaluation in appropriately designed studies. These studies may be prospective clinical trials or retrospective studies based on specimens stored from a completed clinical trial. Ideally, patient treatment assignments should be randomized, and consideration should be given to an appropriate sample size-either for prospective planning of a new study or access to a sufficient number of stored specimens. Here, we develop a novel sample size method for estimation of a confidence interval of specified average width, for an intuitively appealing previously proposed parameter that reflects the expected benefit of using biomarker-guided therapy relative to a standard-of-care therapy. The estimation approach combines Monte Carlo and regression to result in a procedure that performs well over a range of scenarios. Although derived under a specific Cox proportional hazards regression model, robustness to model violations is demonstrated by evaluation under accelerated failure time and cure models. The sample size method produces adequate or conservative sample size estimates under a range of scenarios. Computer code in R and C++, and applications for Mac and Windows are made available for implementation of the sample size estimation procedure. The method is applied to a real data setting and results discussed.

Automated summary

Treatment selection biomarkers are useful in guiding therapy choice and require evaluation in appropriate studies. This study develops a novel sample size method for estimating the benefit of biomarker-guided therapy relative to standard care. The method combines Monte Carlo and regression, and performs well under different scenarios.