New multi-sample nonparametric tests for panel count data

TL;DR

This paper introduces two new nonparametric tests for comparing multiple samples of panel count data, demonstrating improved power over existing methods through theoretical derivations and simulations.

Contribution

The paper develops novel nonparametric test statistics based on accumulated weighted differences, using maximum likelihood estimation for mean functions in panel count data.

Findings

Proposed tests have good finite-sample properties.

New tests outperform existing procedures in simulations.

Applications to real datasets illustrate effectiveness.

Abstract

This paper considers the problem of multi-sample nonparametric comparison of counting processes with panel count data, which arise naturally when recurrent events are considered. Such data frequently occur in medical follow-up studies and reliability experiments, for example. For the problem considered, we construct two new classes of nonparametric test statistics based on the accumulated weighted differences between the rates of increase of the estimated mean functions of the counting processes over observation times, wherein the nonparametric maximum likelihood approach is used to estimate the mean function instead of the nonparametric maximum pseudo-likelihood. The asymptotic distributions of the proposed statistics are derived and their finite-sample properties are examined through Monte Carlo simulations. The simulation results show that the proposed methods work quite well and are more powerful than the existing test procedures. Two real data sets are analyzed and presented as illustrative examples.