Bayesian methods for analysis of binary outcome data in cluster randomized trials on the absolute risk scale

2003 ◽  
Vol 23 (3) ◽  
pp. 389-410 ◽  
Author(s):  
Simon G. Thompson ◽  
David E. Warn ◽  
Rebecca M. Turner
Keyword(s):  
Bayesian Methods ◽  
Randomized Trials ◽  
Absolute Risk ◽  
Outcome Data ◽  
Cluster Randomized Trials ◽  
Binary Outcome ◽  
The Absolute ◽  
Cluster Randomized ◽  
Risk Scale

Response to Is the R coefficient of interest in cluster randomized trials with a binary outcome?

2020 ◽  
Vol 29 (6) ◽  
pp. 1765-1766
Author(s):  
Ariane M Mbekwe Yepnang ◽  
Agnès Caille ◽  
Sandra M Eldridge ◽  
Bruno Giraudeau
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Binary Outcome ◽  
Cluster Randomized

Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness

2019 ◽  
Vol 29 (5) ◽  
pp. 1338-1353
Author(s):  
Elizabeth L Turner ◽  
Lanqiu Yao ◽  
Fan Li ◽  
Melanie Prague
Keyword(s):  
Multiple Imputation ◽  
Randomized Trials ◽  
Simulated Data ◽  
Real Data ◽  
Cluster Randomized Trial ◽  
Outcome Data ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Multilevel Multiple Imputation

The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials.

scholarly journals Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials

2017 ◽  
Vol 17 (2) ◽  
pp. 503-510 ◽  
Author(s):  
Eva Lorenz ◽  
Sabine Gabrysch
Keyword(s):  
Randomized Trials ◽  
Theoretical Background ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Baseline Covariates

In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.

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