|
Abstract:
|
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. 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 method is applied in the context of a biomarker stratified clinical trial design. The estimation approach combines Monte Carlo and regression to result in a procedure that performs well over a range of scenarios. Robustness to model violations is evaluated through accelerated failure time models and cure models. The sample size method produces adequate or conservative sample size estimates. Computer code in R and C++, and applications for Mac and Windows are available.
|
