ArticleStatistics in medicine2015
A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis.
Article in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- An umbrella review on the use of antipsychotics in anxiety disorders: A registered report protocol.PloS one · 2022Article
- How robust are findings of pairwise and network meta-analysis in the presence of missing participant outcome data?BMC medicine · 2021Article
- Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach.Statistical methods in medical research · 2021Article
- Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?BMC medical research methodology · 2021Article
- Effectiveness and adverse events of topical and allergen immunotherapy for atopic dermatitis: a systematic review and network meta-analysis protocol.Systematic reviews · 2020Article
- Article
- Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis.BMC medical research methodology · 2020Article
- Dealing with missing outcome data in meta-analysis.Research synthesis methods · 2020Review
- Participants' outcomes gone missing within a network of interventions: Bayesian modeling strategies.Statistics in medicine · 2019Article
- An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis.BMC medical research methodology · 2019Article
- A systematic survey shows that reporting and handling of missing outcome data in networks of interventions is poor.BMC medical research methodology · 2018Article
- Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches.Systematic reviews · 2015Article
- A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis.Statistics in medicine · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Missing outcome data are a common threat to the validity of the results from randomised controlled trials (RCTs), which, if not analysed appropriately, can lead to misleading treatment effect estimates. Studies with missing outcome data also threaten the validity of any meta-analysis that includes them. A conceptually simple Bayesian framework is proposed, to account for uncertainty due to missing binary outcome data in meta-analysis. A pattern-mixture model is fitted, which allows the incorporation of prior information on a parameter describing the missingness mechanism. We describe several alternative parameterisations, with the simplest being a prior on the probability of an event in the missing individuals. We describe a series of structural assumptions that can be made concerning the missingness parameters. We use some artificial data scenarios to demonstrate the ability of the model to produce a bias-adjusted estimate of treatment effect that accounts for uncertainty. A meta-analysis of haloperidol versus placebo for schizophrenia is used to illustrate the model. We end with a discussion of elicitation of priors, issues with poor reporting and potential extensions of the framework. Our framework allows one to make the best use of evidence produced from RCTs with missing outcome data in a meta-analysis, accounts for any uncertainty induced by missing data and fits easily into a wider evidence synthesis framework for medical decision making.
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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.