Sample Size Calculation for Cluster Randomized Trials with Zero-inflated Count Outcomes

TL;DR

This paper develops a novel sample size calculation method for cluster randomized trials with zero-inflated count outcomes, addressing limitations of traditional models by directly modeling the marginal mean and accounting for zero inflation and clustering effects.

Contribution

It introduces a closed-form sample size formula based on GEE for zero-inflated outcomes, incorporating zero inflation, ICCs, unbalanced randomization, and cluster size variability.

Findings

The method performs well in simulations under various scenarios.

Robust approaches improve accuracy in small samples.

Application to a real trial demonstrates practical utility.

Abstract

Cluster randomized trails (CRT) have been widely employed in medical and public health research. Many clinical count outcomes, such as the number of falls in nursing homes, exhibit excessive zero values. In the presence of zero inflation, traditional power analysis methods for count data based on Poisson or negative binomial distribution may be inadequate. In this study, we present a sample size method for CRTs with zero-inflated count outcomes. It is developed based on GEE regression directly modeling the marginal mean of a ZIP outcome, which avoids the challenge of testing two intervention effects under traditional modeling approaches. A closed-form sample size formula is derived which properly accounts for zero inflation, ICCs due to clustering, unbalanced randomization, and variability in cluster size. Robust approaches, including t-distribution-based approximation and Jackknife re-sampling variance estimator, are employed to enhance trial properties under small sample sizes. Extensive simulations are conducted to evaluate the performance of the proposed method. An application example is presented in a real clinical trial setting.