Planning for subgroup analysis:: a case study of treatment-marker interaction in metastatic colorectal cancer

Subgroup analysis is a common secondary objective in clinical trials. In oncology where the outcome is often binary (such as tumor response) or time-to-event (such as survival), subgroup analysis can be formulated using an interaction term in logistic or proportional hazards regression models. We focus on a case study of planning a randomized trial in metastatic colorectal cancer possibly involving a treatment-marker interaction. We present a method that can be used to compute the power of interaction tests for a given sample size or to compute the necessary sample sizes for a desired level of power for the planned subgroup analysis. The principle idea is borrowed from analysis of variance and uses appropriate contrasts after a variance-stabilizing transformation. This method is conceptually and operationally simple. It can be applied to binary- or ordinal-marker measurements, and existing sample size tables or software can be used. The accuracy of the approximation is shown to be reasonable by simulation studies. (C) 2003 Elsevier Inc. All rights reserved.