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.
Gönen
Mithat
M
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. gonenm@mskcc.org
D014408:Biomarkers, Tumor; D003110:Colonic Neoplasms / Q000188:drug therapy* / Q000401:mortality / Q000473:pathology; D006801:Humans; D007150:Immunohistochemistry; D008113:Liver Neoplasms / Q000556:secondary; D016015:Logistic Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018401:Sample Size; D016019:Survival Analysis
D014408:Biomarkers, Tumor