Sample Size Determination for GEE Analyses of Stepped Wedge Cluster Randomized Trials

In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal foll...

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Veröffentlicht in:	Biometrics Jg. 74; H. 4; S. 1450 - 1458
Hauptverfasser:	Li, Fan, Turner, Elizabeth L., Preisser, John S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley-Blackwell 01.12.2018 Blackwell Publishing Ltd
Schlagworte:	Bias BIOMETRIC PRACTICE: DISCUSSION PAPER biometry Biometry - methods Cluster Analysis Computer Simulation - statistics & numerical data Correlation analysis Cross-Sectional Studies Data processing Eigenvalues equations Estimation Finite sample correction Generalized estimating equations (GEE) Group randomized trials health services Humans Mathematical analysis Matrix‐adjusted estimating equations (MAEE) Parameter estimation Power Randomization Randomized Controlled Trials as Topic Research Design Sample Size Sandwich estimator Size determination variance Wedges Generalized estimating equations (GEE) Group randomized trials Sandwich estimator Finite sample correction Matrix-adjusted estimating equations (MAEE) Power
ISSN:	0006-341X, 1541-0420, 1541-0420
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Abstract	In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator.
AbstractList	In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator. Summary In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow‐up, proper sample size calculation should account for three distinct types of intraclass correlations: the within‐period, the inter‐period, and the within‐individual correlations. Setting the latter two correlation parameters to be equal accommodates cross‐sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within‐cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias‐corrected estimating equations for the correlation parameters concurrently with a bias‐corrected sandwich variance estimator. In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator.In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator.
Author	Li, Fan Turner, Elizabeth L. Preisser, John S.
AuthorAffiliation	3 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A 1 Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710, U.S.A 2 Duke Global Health Institute, Durham, North Carolina 27708, U.S.A
AuthorAffiliation_xml	– name: 3 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A – name: 2 Duke Global Health Institute, Durham, North Carolina 27708, U.S.A – name: 1 Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710, U.S.A
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Keywords	Generalized estimating equations (GEE) Group randomized trials Sandwich estimator Finite sample correction Matrix-adjusted estimating equations (MAEE) Power
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Snippet	In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such... Summary In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards....
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SubjectTerms	Bias BIOMETRIC PRACTICE: DISCUSSION PAPER biometry Biometry - methods Cluster Analysis Computer Simulation - statistics & numerical data Correlation analysis Cross-Sectional Studies Data processing Eigenvalues equations Estimation Finite sample correction Generalized estimating equations (GEE) Group randomized trials health services Humans Mathematical analysis Matrix‐adjusted estimating equations (MAEE) Parameter estimation Power Randomization Randomized Controlled Trials as Topic Research Design Sample Size Sandwich estimator Size determination variance Wedges
Title	Sample Size Determination for GEE Analyses of Stepped Wedge Cluster Randomized Trials
URI	https://www.jstor.org/stable/45093012 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fbiom.12918 https://www.ncbi.nlm.nih.gov/pubmed/29921006 https://www.proquest.com/docview/2175458013 https://www.proquest.com/docview/2057444652 https://www.proquest.com/docview/2221034739 https://pubmed.ncbi.nlm.nih.gov/PMC6461045
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