Evidence map›Paper›PMID 22016450›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making

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

Manuel Gomes, Edmond S-W Ng, Richard Grieve, Richard Nixon, James Carpenter, Simon G Thompson

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  20. Point-of-care urine culture for managing urinary tract infection in primary care: a randomised controlled trial of clinical and cost-effectiveness.The British journal of general practice : the journal of the Royal College of General Practitioners · 2018
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17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Manuel GomesDepartment of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, ESWN, RG)
Edmond S-W NgDepartment of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, ESWN, RG)
Richard GrieveDepartment of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, ESWN, RG)
Richard NixonModeling and Simulation Group, Novartis Pharma AG, Basel, Switzerland (RN)
James CarpenterDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK (JC)
Simon G ThompsonMRC Biostatistics Unit, Cambridge, UK (SGT)

Funding

British Heart Foundation RG/08/014/24067Medical Research Council G0802321Medical Research Council MC_U105260792
6 · The paper itself

Abstract

aimCost-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.

methodsOur 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.

resultsEach 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.

conclusionsMLMs 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.

Indexed as

Cluster AnalysisAdultBiasCost-Benefit AnalysisDecision MakingFollow-Up StudiesHealth Care CostsHumansLung DiseasesMonte Carlo MethodMultilevel AnalysisOutcome and Process Assessment, Health CarePatient Education as TopicQuality-Adjusted Life YearsRandomized Controlled Trials as TopicRegression Analysis

Identifiers

PMID22016450
PMCPMC3757919

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.