Statistics in Clinical Cancer Research Comparison of Statistical Analysis Plans in Randomize-All Phase III Trials with a Predictive Biomarker

When there are no compelling biologic or early trial data for a candidate predictive biomarker with regard to its ability to predict the effect of an anticancer treatment at the initiation of definitive phase III trials, it is generally reasonable to include all patients as eligible for randomization but to plan for a prospective subgroup analysis based on the biomarker. We assessed such statistical analysis plans, fixed-sequence, fallback, and treatment-by-biomarker interaction approaches, in terms of the probability of asserting treatment efficacy for either the overall patient population or a biomarker-positive subpopulation of patients. If there was some evidence that the treatment would work better in the biomarker-positive subgroup than the biomarker-negative subgroup, then the fixed-sequence approaches would be favored, whereas if evidence was weak that there would be much difference in responsiveness between the two subgroups, then the fallback approach would be favored. If there was substantial uncertainty in the difference in treatment effects between the two subgroups, the treatment-by-biomarker interaction approach could be a reasonable choice as this approach generally provided a high probability of asserting treatment efficacy for the right patient population under homogeneous treatment effects and a qualitative interaction over biomarker-based subgroups. Clin Cancer Res; 20(11); 2820–30. 2014 AACR.