ArticleBMC medical research methodology2020
A framework for extending trial design to facilitate missing data sensitivity analyses.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reduced exposure to vasopressors through permissive hypotension to reduce mortality in critically ill people aged 65 and over: the 65 RCT.Health technology assessment (Winchester, England) · 2021Pooled it
- Determining the sample size for a cluster-randomised trial using knowledge elicitation: Bayesian hierarchical modelling of the intracluster correlation coefficient.Clinical trials (London, England) · 2023Trial
- Comprehensive implementations of multiple imputation using retrieved dropouts for continuous endpoints.BMC medical research methodology · 2025Article
- Visual and Anatomical Outcomes of a Single Intravitreal Dexamethasone in Diabetic Macular Edema: An 8 Year Real-World Study.Journal of clinical medicine · 2023Article
- Missing data: A statistical framework for practice.Biometrical journal. Biometrische Zeitschrift · 2021Review
- Insights From Survival Analyses During 12 Years of Anti-Vascular Endothelial Growth Factor Therapy for Neovascular Age-Related Macular Degeneration.JAMA ophthalmology · 2021Observational
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6 authors.
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Abstract
backgroundMissing data are an inevitable challenge in Randomised Controlled Trials (RCTs), particularly those with Patient Reported Outcome Measures. Methodological guidance suggests that to avoid incorrect conclusions, studies should undertake sensitivity analyses which recognise that data may be 'missing not at random' (MNAR). A recommended approach is to elicit expert opinion about the likely outcome differences for those with missing versus observed data. However, few published trials plan and undertake these elicitation exercises, and so lack the external information required for these sensitivity analyses. The aim of this paper is to provide a framework that anticipates and allows for MNAR data in the design and analysis of clinical trials.
methodsWe developed a framework for performing and using expert elicitation to frame sensitivity analysis in RCTs with missing outcome data. The framework includes the following steps: first defining the scope of the elicitation exercise, second developing the elicitation tool, third eliciting expert opinion about the missing outcomes, fourth evaluating the elicitation results, and fifth analysing the trial data. We provide guidance on key practical challenges that arise when adopting this approach in trials: the criteria for identifying relevant experts, the outcome scale for presenting data to experts, the appropriate representation of expert opinion, and the evaluation of the elicitation results.The framework was developed within the POPPI trial, which investigated whether a preventive, complex psychological intervention, commenced early in ICU, would reduce the development of patient-reported post-traumatic stress disorder symptom severity, and improve health-related quality of life. We illustrate the key aspects of the proposed framework using the POPPI trial.
resultsFor the POPPI trial, 113 experts were identified with potentially suitable knowledge and asked to participate in the elicitation exercise. The 113 experts provided 59 usable elicitation questionnaires. The sensitivity analysis found that the results from the primary analysis were robust to alternative MNAR mechanisms.
conclusionsFuture studies can adopt this framework to embed expert elicitation within the design of clinical trials. This will provide the information required for MNAR sensitivity analyses that examine the robustness of the trial conclusions to alternative, but realistic assumptions about the missing data.
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