Proportional mean model for panel count data with multiple modes of recurrence

TL;DR

This paper introduces a proportional mean model for analyzing panel count data with multiple recurrence modes, estimating covariate effects and baseline functions, validated through simulations and applied to skin cancer trial data.

Contribution

It proposes a novel proportional mean model for multi-mode recurrence panel count data, including estimation procedures and asymptotic properties.

Findings

Estimators have good finite sample properties in simulations.

Model effectively captures effects of covariates on recurrence modes.

Application demonstrates practical utility in medical studies.

Abstract

Panel count data is common when the study subjects are exposed to recurrent events, observed only at discrete time points. In this article, we consider the regression analysis of panel count data with multiple modes of recurrence. We propose a proportional mean model to estimate the effect of covariates on the underlying counting process due to different modes of recurrence. The simultaneous estimation of baseline cumulative mean functions and regression parameters of $(k>1)$ recurrence modes are studied in detail. Asymptotic properties of the proposed estimators are also established. A Monte Carlo simulation study is carried out to validate the finite sample behaviour of the proposed estimators. The methods are applied to a real data arising from skin cancer chemoprevention trial.