Testing the homogeneity of risk differences with sparse count data

TL;DR

This paper introduces a new statistical test for assessing the homogeneity of risk differences in sparse binomial data, addressing limitations of existing methods in size control and power, supported by theoretical and empirical evidence.

Contribution

The paper proposes a novel test for risk difference homogeneity in sparse binomial data that improves upon existing methods in size accuracy and power, with proven asymptotic properties.

Findings

The proposed test maintains nominal size better than existing tests.

It demonstrates higher power in detecting heterogeneity in simulations.

Real data applications confirm its practical reliability.

Abstract

In this paper, we consider testing the homogeneity of risk differences in independent binomial distributions especially when data are sparse. We point out some drawback of existing tests in either controlling a nominal size or obtaining powers through theoretical and numerical studies. The proposed test is designed to avoid such drawback of existing tests. We present the asymptotic null distributions and asymptotic powers for our proposed test. We also provide numerical studies including simulations and real data examples showing the proposed test has reliable results compared to existing testing procedures.