Power considerations for generalized estimating equations analyses of four-level cluster randomized trials

TL;DR

This paper develops methods for calculating sample size and power in four-level cluster randomized trials, addressing complex multi-level clustering and providing formulas applicable to various intervention levels.

Contribution

It introduces closed-form sample size formulas for four-level CRTs using GEE, accounting for multiple intraclass correlations and different intervention assignment levels.

Findings

Empirical power aligns well with predicted power using the proposed formulas.

Methods are effective even with as few as 8 clusters.

Approach works for both balanced and unbalanced designs.

Abstract

In this article, we develop methods for sample size and power calculations in four-level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs involving four levels are becoming popular in health care research, where the effects are measured, for example, from evaluations (level 1) within participants (level 2) in divisions (level 3) that are nested in clusters (level 4). In such multi-level CRTs, we consider three types of intraclass correlations between different evaluations to account for such clustering: that of the same participant, that of different participants from the same division, and that of different participants from different divisions in the same cluster. Assuming arbitrary link and variance functions, with the proposed correlation structure as the true correlation structure, closed-form sample size formulas for randomization carried out at any level (including individually randomized trials within a four-level clustered structure) are derived based on the generalized estimating equations approach using the model-based variance and using the sandwich variance with an independence working correlation matrix. We demonstrate that empirical power corresponds well with that predicted by the proposed method for as few as 8 clusters, when data are analyzed using the matrix-adjusted estimating equations for the correlation parameters with a bias-corrected sandwich variance estimator, under both balanced and unbalanced designs.