ArticleHealth economics2018
A Bayesian framework for health economic evaluation in studies with missing data.
Article in Health economics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations.PharmacoEconomics · 2023Pooled it
- Benefits of aldosterone receptor antagonism in chronic kidney disease: the BARACK-D RCT.Health technology assessment (Winchester, England) · 2025Trial
- Missing Data in Trial-Based Cost-Effectiveness Analysis: The Journey Continues.PharmacoEconomics · 2026Review
- A scoping review of statistical methods for trial-based economic evaluations: The current state of play.Health economics · 2022Article
- Linear mixed models to handle missing at random data in trial-based economic evaluations.Health economics · 2022Article
- Comparing methods for handling missing cost and quality of life data in the Early Endovenous Ablation in Venous Ulceration trial.Cost effectiveness and resource allocation : C/E · 2022Article
- Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data.Health economics · 2021Article
- A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis.Medical decision making : an international journal of the Society for Medical Decision Making · 2021Article
- The statistical approach in trial-based economic evaluations matters: get your statistics together!BMC health services research · 2021Article
- A framework for extending trial design to facilitate missing data sensitivity analyses.BMC medical research methodology · 2020Article
- Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis.Health economics · 2020Article
- A Bayesian framework for health economic evaluation in studies with missing data.Health economics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Health economics studies with missing data are increasingly using approaches such as multiple imputation that assume that the data are "missing at random." This assumption is often questionable, as-even given the observed data-the probability that data are missing may reflect the true, unobserved outcomes, such as the patients' true health status. In these cases, methodological guidelines recommend sensitivity analyses to recognise data may be "missing not at random" (MNAR), and call for the development of practical, accessible approaches for exploring the robustness of conclusions to MNAR assumptions. Little attention has been paid to the problem that data may be MNAR in health economics in general and in cost-effectiveness analyses (CEA) in particular. In this paper, we propose a Bayesian framework for CEA where outcome or cost data are missing. Our framework includes a practical, accessible approach to sensitivity analysis that allows the analyst to draw on expert opinion. We illustrate the framework in a CEA comparing an endovascular strategy with open repair for patients with ruptured abdominal aortic aneurysm, and provide software tools to implement this approach.
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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.