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. 2020 Dec 10;39(28):4218-4237.
doi: 10.1002/sim.8721. Epub 2020 Aug 21.

Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials

Siyun Yang  1 , Fan Li  2   3 , Monique A Starks  4   5 , Adrian F Hernandez  4   5 , Robert J Mentz  4   5 , Kingshuk R Choudhury  1
Affiliations

Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials

Siyun Yang et al. Stat Med. .

Abstract

Cluster randomized trials (CRTs) refer to experiments with randomization carried out at the cluster or the group level. While numerous statistical methods have been developed for the design and analysis of CRTs, most of the existing methods focused on testing the overall treatment effect across the population characteristics, with few discussions on the differential treatment effect among subpopulations. In addition, the sample size and power requirements for detecting differential treatment effect in CRTs remain unclear, but are helpful for studies planned with such an objective. In this article, we develop a new sample size formula for detecting treatment effect heterogeneity in two-level CRTs for continuous outcomes, continuous or binary covariates measured at cluster or individual level. We also investigate the roles of two intraclass correlation coefficients (ICCs): the adjusted ICC for the outcome of interest and the marginal ICC for the covariate of interest. We further derive a closed-form design effect formula to facilitate the application of the proposed method, and provide extensions to accommodate multiple covariates. Extensive simulations are carried out to validate the proposed formula in finite samples. We find that the empirical power agrees well with the prediction across a range of parameter constellations, when data are analyzed by a linear mixed effects model with a treatment-by-covariate interaction. Finally, we use data from the HF-ACTION study to illustrate the proposed sample size procedure for detecting heterogeneous treatment effects.

Keywords: cluster randomized trials; heterogeneous treatment effect; interaction; intraclass correlation coefficient; power formula; sample size estimation.

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Figures

FIGURE 1
FIGURE 1
Variance of the GLS estimator for the treatment-by-covariate interaction, σ42, as a function of the, A, covariate ICC ρx and, B, adjusted outcome ICC ρy|x with cluster sizes m∈{20,50,100}, assuming σy∣x2=σx2=1, and σw2=1/4
FIGURE 2
FIGURE 2
Ratio of total sample size required for testing HTE vs OTE as a function of the cluster size m, covariate ICC ρx, adjusted outcome ICC ρy|x, and ratio of detectable effect sizes (RDES), assuming σx2=σy∣x2=1, β2 = β3 = 0.5, and W = 1/2
FIGURE 3
FIGURE 3
Distributions of proportion of black population and mean age across 82 sites (clusters) in the HF-ACTION study
See this image and copyright information in PMC

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