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A framework for extending trial design to facilitate missing data sensitivity analyses

BACKGROUND: Missing 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...

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Autores principales: Mason, Alexina J., Grieve, Richard D., Richards-Belle, Alvin, Mouncey, Paul R., Harrison, David A., Carpenter, James R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076973/
https://www.ncbi.nlm.nih.gov/pubmed/32183708
http://dx.doi.org/10.1186/s12874-020-00930-2
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author Mason, Alexina J.
Grieve, Richard D.
Richards-Belle, Alvin
Mouncey, Paul R.
Harrison, David A.
Carpenter, James R.
author_facet Mason, Alexina J.
Grieve, Richard D.
Richards-Belle, Alvin
Mouncey, Paul R.
Harrison, David A.
Carpenter, James R.
author_sort Mason, Alexina J.
collection PubMed
description BACKGROUND: Missing 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. METHODS: We 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. RESULTS: For 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. CONCLUSIONS: Future 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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spelling pubmed-70769732020-03-18 A framework for extending trial design to facilitate missing data sensitivity analyses Mason, Alexina J. Grieve, Richard D. Richards-Belle, Alvin Mouncey, Paul R. Harrison, David A. Carpenter, James R. BMC Med Res Methodol Technical Advance BACKGROUND: Missing 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. METHODS: We 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. RESULTS: For 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. CONCLUSIONS: Future 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. BioMed Central 2020-03-17 /pmc/articles/PMC7076973/ /pubmed/32183708 http://dx.doi.org/10.1186/s12874-020-00930-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Mason, Alexina J.
Grieve, Richard D.
Richards-Belle, Alvin
Mouncey, Paul R.
Harrison, David A.
Carpenter, James R.
A framework for extending trial design to facilitate missing data sensitivity analyses
title A framework for extending trial design to facilitate missing data sensitivity analyses
title_full A framework for extending trial design to facilitate missing data sensitivity analyses
title_fullStr A framework for extending trial design to facilitate missing data sensitivity analyses
title_full_unstemmed A framework for extending trial design to facilitate missing data sensitivity analyses
title_short A framework for extending trial design to facilitate missing data sensitivity analyses
title_sort framework for extending trial design to facilitate missing data sensitivity analyses
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076973/
https://www.ncbi.nlm.nih.gov/pubmed/32183708
http://dx.doi.org/10.1186/s12874-020-00930-2
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