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Ratio estimators of intervention effects on event rates in cluster randomized trials
Author(s) -
Ma Xiangmei,
Milligan Paul,
Lam Kwok Fai,
Cheung Yin Bun
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9226
Subject(s) - estimator , statistics , event (particle physics) , randomized controlled trial , cluster (spacecraft) , cluster randomised controlled trial , econometrics , medicine , mathematics , computer science , physics , quantum mechanics , programming language
Abstract We consider five asymptotically unbiased estimators of intervention effects on event rates in non‐matched and matched‐pair cluster randomized trials, including ratio of mean countsr 1, ratio of mean cluster‐level event ratesr 2, ratio of event ratesr 3, double ratio of countsr 4, and double ratio of event ratesr 5. In the absence of an indirect effect, they all estimate the direct effect of the intervention. Otherwise,r 1 ,r 2 , andr 3estimate the total effect, which comprises the direct and indirect effects, whereasr 4andr 5estimate the direct effect only. We derive the conditions under which each estimator is more precise or powerful than its alternatives. To control bias in studies with a small number of clusters, we propose a set of approximately unbiased estimators. We evaluate their properties by simulation and apply the methods to a trial of seasonal malaria chemoprevention. The approximately unbiased estimators are practically unbiased and their confidence intervals usually have coverage probability close to the nominal level; the asymptotically unbiased estimators perform well when the number of clusters is approximately 32 or more per trial arm. Despite its simplicity,r 1performs comparably withr 2andr 3in trials with a large but realistic number of clusters. When the variability of baseline event rate is large and there is no indirect effect,r 4andr 5tend to offer higher power thanr 1 ,r 2 , andr 3 . We discuss the implications of these findings to the planning and analysis of cluster randomized trials.