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



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.



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.



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.
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, ESWN, RG)
Manuel
M
Gomes
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, ESWN, RG)
Edmond S-W
ES
Ng
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, ESWN, RG)
Richard
R
Grieve
Modeling and Simulation Group, Novartis Pharma AG, Basel, Switzerland (RN)
Richard
R
Nixon
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK (JC)
James
J
Carpenter
MRC Biostatistics Unit, Cambridge, UK (SGT)
Simon G
SG
Thompson
D000328:Adult; D015982:Bias; D016000:Cluster Analysis; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data*; D003657:Decision Making; D005500:Follow-Up Studies; D017048:Health Care Costs / Q000706:statistics & numerical data; D006801:Humans; D008171:Lung Diseases / Q000628:therapy; D009010:Monte Carlo Method; D055361:Multilevel Analysis; D010043:Outcome and Process Assessment, Health Care / Q000191:economics; D010353:Patient Education as Topic; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic / Q000191:economics*; D012044:Regression Analysis; D013019:South Africa; D013223:Statistics as Topic; D018709:Statistics, Nonparametric