MULTIVARIATE TESTS FOR MULTIPLE END-POINTS IN CLINICAL-TRIALS

Clinical trials frequently lack a single definitive endpoint that completely describes treatment efficacy. When a treatment affects a disease in a multitude of ways, several endpoints are necessary to describe efficacy. There is a variety of statistical procedures to provide a single p-value when a treatment affects several endpoints. This paper reviews several procedures including Hotelling's T-2 test, an approximate likelihood ratio test (Tang et al.), the weighted version of O'Brien's test, tests involving the maximum of several test statistics, and a test based on the average of the maximum of several endpoints (Wittes). I propose a risk score test whose rejection boundary corresponds to a contour of constant risk. Calculations and simulation studies help to compare the different tests with an emphasis on the effect of non-standard alternatives, and on identifying settings where some tests may lack clinical relevance.