Robust Tests for Treatment Effects Based on Censored Recurrent Event Data Observed over Multiple Periods

We derive semiparametric methods for estimating and testing treatment effects when cen­sored recurrent event data are available over multiple periods. These methods are based on estimating functions motivated by a working "mixed-Poisson" assumption under which conditioning can eliminate subject-specific random effects. Robust pseudoscore test statistics are obtained via "sandwich" variance estimation. The relative efficiency of conditional versus marginal analyses is assessed analytically under a mixed timehomogeneous Poisson model. The robustness and empirical power of the semiparametric ap­proach are assessed through simulation. Adaptations to handle recurrent events arising in crossover trials are described and these methods are applied to data from a two-period crossover trial of patients with bronchial asthma.