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

Publication ,  Journal Article
Yang, S; Li, F; Starks, MA; Hernandez, AF; Mentz, RJ; Choudhury, KR
Published in: Stat Med
December 10, 2020

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.

Duke Scholars

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

December 10, 2020

Volume

39

Issue

28

Start / End Page

4218 / 4237

Location

England

Related Subject Headings

  • Statistics & Probability
  • Sample Size
  • Research Design
  • Randomized Controlled Trials as Topic
  • Linear Models
  • Humans
  • Cluster Analysis
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
 

Citation

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Yang, S., Li, F., Starks, M. A., Hernandez, A. F., Mentz, R. J., & Choudhury, K. R. (2020). Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials. Stat Med, 39(28), 4218–4237. https://doi.org/10.1002/sim.8721
Yang, Siyun, Fan Li, Monique A. Starks, Adrian F. Hernandez, Robert J. Mentz, and Kingshuk R. Choudhury. “Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials.Stat Med 39, no. 28 (December 10, 2020): 4218–37. https://doi.org/10.1002/sim.8721.
Yang S, Li F, Starks MA, Hernandez AF, Mentz RJ, Choudhury KR. Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials. Stat Med. 2020 Dec 10;39(28):4218–37.
Yang, Siyun, et al. “Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials.Stat Med, vol. 39, no. 28, Dec. 2020, pp. 4218–37. Pubmed, doi:10.1002/sim.8721.
Yang S, Li F, Starks MA, Hernandez AF, Mentz RJ, Choudhury KR. Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials. Stat Med. 2020 Dec 10;39(28):4218–4237.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

December 10, 2020

Volume

39

Issue

28

Start / End Page

4218 / 4237

Location

England

Related Subject Headings

  • Statistics & Probability
  • Sample Size
  • Research Design
  • Randomized Controlled Trials as Topic
  • Linear Models
  • Humans
  • Cluster Analysis
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services