Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis.
Health Econ
; 29(2): 171-184, 2020 02.
Article
em En
| MEDLINE
| ID: mdl-31845455
ABSTRACT
Missing data are a common issue in cost-effectiveness analysis (CEA) alongside randomised trials and are often addressed assuming the data are 'missing at random'. However, this assumption is often questionable, and sensitivity analyses are required to assess the implications of departures from missing at random. Reference-based multiple imputation provides an attractive approach for conducting such sensitivity analyses, because missing data assumptions are framed in an intuitive way by making reference to other trial arms. For example, a plausible not at random mechanism in a placebo-controlled trial would be to assume that participants in the experimental arm who dropped out stop taking their treatment and have similar outcomes to those in the placebo arm. Drawing on the increasing use of this approach in other areas, this paper aims to extend and illustrate the reference-based multiple imputation approach in CEA. It introduces the principles of reference-based imputation and proposes an extension to the CEA context. The method is illustrated in the CEA of the CoBalT trial evaluating cognitive behavioural therapy for treatment-resistant depression. Stata code is provided. We find that reference-based multiple imputation provides a relevant and accessible framework for assessing the robustness of CEA conclusions to different missing data assumptions.
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Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
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Interpretação Estatística de Dados
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Modelos Estatísticos
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Análise Custo-Benefício
Tipo de estudo:
Clinical_trials
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Diagnostic_studies
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Health_economic_evaluation
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Health Econ
Assunto da revista:
SERVICOS DE SAUDE
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Reino Unido