Marginal versus conditional rate estimation for count and recurrent event data with an estimand framework

Abstract

Count and recurrent event endpoints are common key efficacy endpoints in clinical research. For example, in clinical research of pulmonary diseases such as chronic obstructive pulmonary disease (COPD) or asthma, the reduction of the occurrence of a recurrent event, pulmonary exacerbation (PEx) caused by acute respiratory symptoms, is often used to measure the treatment effect. The occurrence of PEx is often analyzed with nonlinear models, such as Poisson regression or Negative Binomial regression. It is observed that model-estimated within-group PEx rates are often lower than the descriptive statistics of within-group PEx rates. Motivated by this observation, we explore their relationship mathematically and demonstrate that it is due to the difference between conditional PEx rates and population-level PEx rates (marginal rates). Our findings corroborate the recent FDA guidance (2023) [1], which discusses considerations for covariate adjustment in nonlinear models, and that conditional or subgroup treatment effects with covariate adjustment may differ from marginal treatment effects. In this article, we demonstrate how covariate adjustment impacts the estimation of event rates and rate ratios with both closed form and simulation studies. Additionally, following the ICH E9 addendum on the estimand framework [2], we discuss the estimand framework for count and recurrent event data.