Alternate Statistical Tools and Limitations in Genetic Marker Association Studies in Single-Arm Drug Cancer Trials

In this issue of the Journal, Graziano et al 1 investigate the associations of a number of genotype and haplotype markers with clinical outcome in a sample of patients with metastatic colorectal cancer receiving salvage cetuximab plus irinotecan therapy. This Editorial will review of some of the statistical issues in the results and discussions.Genotypeandhaplotypesmarkerswillbecollectivelyreferredto as genetic markers. In genetic marker association studies, the first set of analyses commonly consists of the investigation of potential pairwise associations between each genetic marker and the clinical outcome of interest.Theseunivariateanalyses,astheyarecommonlyreferredtointhe medicalliterature,arethenfollowedbyaninvestigationoftheeffectof adjustingforotherpotentiallyimportantmolecular,clinical,ordemographic variables. These multivariate analyses are commonly carried out using log-linear parametric logistic models 2 for binary outcomes and log-linear semiparametric Cox proportional hazards models 3 for time-to-event outcomes subject to censoring. The employment of these models is attractive, as they provide a unified framework for statistical inference on the variables and point and interval estimation of the effects (odds ratios for binary outcomes and hazard ratios for time-to-event outcomes). The use of these methods is also attractive from a practical point of view, as they have been widely implemented in commercial software distributions that allow the user, from the novice to the expert, to generate massive amount of output through graphical user interfaces. As with any model, one needs to be concerned about robustness ofthefindingswithrespecttodeviationsfromthemodelassumptions. Considerable deviations may not only invalidate the inferences but mayalsorendertheinterpretationoftheresultsdifficult.Forexample,