Sequential Testing with Recurrent Events over Multiple Treatment Periods

Abstract

Recurrent event outcomes are adopted increasingly often as a basis for evaluating experimental interventions. In clinical trials involving recurrent events, patients are frequently observed for a baseline period while under standard care, and then randomised to receive either an experimental treatment or continue on standard care. When events are generated according to a mixed Poisson model, having baseline data permits a conditional analysis which can eliminate the subject-specific random effect and yield a more efficient analysis regarding treatment effect. When studies are expected to recruit a large number of patients over an extended period of accrual, or if the period of follow-up is long, sequential testing is desirable to ensure the study is stopped as soon as sufficient data have been collected to establish treatment benefits. We describe methods which facilitate sequential analysis of data arising from trials with recurrent event responses observed over two treatment periods where one is a baseline period of observation. Formulae to help schedule analyses at approximately equal increments of information are given. Simulation studies show that the sequential testing procedures have rejection rates compatible with the nominal error rates under the null and alternative hypotheses. Data from a trial of patients with herpes simplex virus infection are analysed to illustrate the utility of these methods.