Novel Non-Negative Variance Estimator for (Modified) Within-Cluster Resampling

TL;DR

This paper introduces a new positive variance estimator for within-cluster resampling methods, improving statistical accuracy and power in longitudinal data analysis.

Contribution

It proposes a novel non-negative variance estimator for WCR and MWCR, addressing the issue of negative variance estimates and enhancing their practical utility.

Findings

The new estimator is strictly positive in practice.

Simulations show it preserves type I error.

It enables power gains in MWCR.

Abstract

This article proposes a novel variance estimator for within-cluster resampling (WCR) and modified within-cluster resampling (MWCR) - two existing methods for analyzing longitudinal data. WCR is a simple but computationally intensive method, in which a single observation is randomly sampled from each cluster to form a new dataset. This process is repeated numerous times, and in each resampled dataset (or outputation), we calculate beta using a generalized linear model. The final resulting estimator is an average across estimates from all outputations. MWCR is an extension of WCR that can account for the within-cluster correlation of the dataset; consequently, there are two noteworthy differences: 1) in MWCR, each resampled dataset is formed by randomly sampling multiple observations without replacement from each cluster and 2) generalized estimating equations (GEEs) are used to estimate the parameter of interest. While WCR and MWCR are relatively simple to implement, a key challenge is that the proposed moment-based estimator is often times negative in practice. Our modified variance estimator is not only strictly positive, but simulations show that it preserves the type I error and allows statistical power gains associated with MWCR to be realized.