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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Vydáno v:Biometrics Ročník 74; číslo 4; s. 1450 - 1458
Hlavní autoři: Li, Fan, Turner, Elizabeth L., Preisser, John S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley-Blackwell 01.12.2018
Blackwell Publishing Ltd
Témata:
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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Popis
Shrnutí: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.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12918