Interim monitoring of clinical trials based on long-term binary endpoints.

Abstract

This paper discusses interim analysis of randomized clinical trials for which the primary endpoint is observed at a specific long-term follow-up time. For such trials subjects only yield direct information on the primary endpoint once they have been followed through to the long-term follow-up time, potentially eliminating a large proportion of the accrued sample from an interim analysis of the primary endpoint. We advocate more efficient interim analysis of long-term endpoints by augmenting long-term information with short-term information on subjects who have not yet been followed through to the long-term follow-up time. While retaining the long-term endpoint as the subject of the analysis, methods of jointly analysing short- and long-term data are discussed for reversible binary endpoints. It is shown theoretically and by simulation that the use of short-term information improves the efficiency with which long-term treatment differences are assessed based on interim data. Sequential analysis of treatment differences is discussed based on spending functions, and is illustrated with a numerical example from a cholesterol treatment trial.