Med Decis Making - Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

Tópicos

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Resumo

M: Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs.METHODS: Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study.RESULTS: Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research.CONCLUSIONS: MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Resumo Limpo

m costeffect analys cea may use data cluster random trial crts unit random cluster individu howev studi use analyt method ignor cluster articl compar altern statist method accommod cluster cea crtsmethod simul studi compar perform statist method cea crts treatment arm studi consid method ignor clusteringseem unrel regress sur without robust standard error seand method recogn clusteringsur general estim equat gee robust se stage nonparametr bootstrap tsb shrinkag correct multilevel model mlm base case assum crts moder number balanc cluster per arm normal distribut cost scenario includ crts cluster imbalanc cluster size skew cost perform report bias root mean squar error rmse confid interv ci coverag estim increment net benefit inb also compar method case studyresult method report low level bias without robust se sur gave poor ci coverag base case v nomin level mlm tsb perform well scenario ci coverag cluster gee sur robust se coverag case studi mean inb similar across method ignor cluster underestim statist uncertainti valu researchconclus mlms tsb appropri analyt method cea crts characterist describ sur gee recommend studi cluster

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