Analyzing Zero-Truncated Recurrent Events by Stratified Regression with Time-Varying Coefficients

TL;DR

This paper develops a stratified Cox regression model with time-varying coefficients for analyzing zero-truncated recurrent event data, motivated by pediatric mental health care, and demonstrates its effectiveness through simulations and real data.

Contribution

It introduces a novel estimation procedure for stratified Cox models with time-varying coefficients using zero-truncated data and population information.

Findings

Estimator performs well in finite samples.

Asymptotic properties are established.

Application to PMHC data illustrates practical utility.

Abstract

This paper presents a strategy for analyzing zero-truncated recurrent events data. Motivated by a pediatric mental health care (PMHC) program, we are particularly concerned with how the event occurrence depends on the occurrences in the past. We consider a stratified Cox regression model with time-varying coefficients and propose a procedure for estimating the model parameters using the zero-truncated data integrated with population census information. We evaluate the finite-sample performance of the proposed estimator through simulation and establish its asymptotic properties. Data from the PMHC program are used throughout the paper to motivate and to illustrate the proposed approach.