Pseudo-value Based Mean Cumulative Count Regression

2026-06-22

AI Summary

This study addresses recurrent event data subject to right censoring and a terminal event by proposing a pseudo-value–based regression approach to model the effects of covariates on the mean cumulative function (MCF) and its area under the curve (AUMCF) at fixed time points. The method systematically extends the pseudo-value regression framework to estimate covariate effects on both MCF and AUMCF, constructing pseudo-observations via influence functions and enabling efficient inference through generalized estimating equations or ordinary least squares. Computationally straightforward and interpretable, the approach is compatible with standard regression software. Simulation studies demonstrate its favorable performance across diverse recurrent event settings, exhibiting accurate estimation, proper confidence interval coverage, controlled Type I error rates, and high statistical power. The method is successfully applied to the ORATORIO clinical trial data.

Abstract

The mean cumulative function (MCF) summarizes how events accumulate over time for a recurrent or multi-component endpoint. The MCF, and its integral over a given time horizon, the area under the MCF (AUMCF), provide interpretable summaries of recurrent-event burden in the presence of right-censoring and terminal events. Existing approaches for these estimands have focused primarily on nonparametric treatment comparisons, covariate-adjusted augmentation, and linearized test statistics. Herein, we propose a pseudo-value-based regression approach for estimating covariate effects on the MCF and AUMCF at a fixed truncation time. The proposed method uses influence-function-based pseudo-values as regression outcomes, allowing estimation with standard generalized estimating equation machinery and, under an identity link, ordinary least squares. Through simulation studies, we evaluate estimation accuracy, confidence interval coverage, type I error control, and power across a range of recurrent-event settings. We demonstrate the utility of the proposed covariate adjustment procedure through an application to the ORATORIO clinical trial, evaluating the safety and efficacy of ocrelizumab for the treatment of primary progressive multiple sclerosis. Overall, pseudo-value-based regression provides a simple and interpretable framework for modeling covariate effects on cumulative recurrent-event burden over time.

Problem

• mean cumulative function
• recurrent events
• covariate effects
• right-censoring
• pseudo-values

Innovation

• pseudo-value
• mean cumulative function
• recurrent events
• generalized estimating equations
• covariate adjustment