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

Alexina J Mason1, Richard D Grieve2, Alvin Richards-Belle3

  • 1Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK. alexina.mason@lshtm.ac.uk.

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|March 19, 2020
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Summary
This summary is machine-generated.

This study introduces a framework for expert elicitation to handle missing data in clinical trials. Sensitivity analyses using this framework confirmed the robustness of trial results to missing not at random data.

Keywords:
Bayesian analysisClinical trialsExpert elicitationMissing dataPattern-mixture modelsSensitivity analysis

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Psychological Interventions

Background:

  • Missing data are common in Randomized Controlled Trials (RCTs), especially with Patient Reported Outcome Measures.
  • Methodological guidance recommends sensitivity analyses for data 'missing not at random' (MNAR).
  • Eliciting expert opinion is crucial for MNAR sensitivity analyses but often not planned in trials.

Purpose of the Study:

  • To provide a framework for anticipating and managing MNAR data in clinical trial design and analysis.
  • To integrate expert elicitation into sensitivity analyses for RCTs with missing outcome data.

Main Methods:

  • Developed a five-step framework: defining scope, developing tools, eliciting opinions, evaluating results, and analyzing data.
  • Addressed practical challenges like expert selection, data presentation, and opinion representation.
  • Applied the framework within the POPPI trial, investigating a psychological intervention for PTSD symptom severity and quality of life.

Main Results:

  • Identified 113 experts, with 59 providing usable questionnaires for the POPPI trial.
  • Sensitivity analysis demonstrated that primary analysis results were robust to alternative MNAR mechanisms.
  • The expert elicitation successfully provided necessary information for sensitivity analyses.

Conclusions:

  • Future studies can adopt this framework to embed expert elicitation in clinical trial design.
  • This approach provides essential information for MNAR sensitivity analyses.
  • It enhances the examination of trial conclusion robustness under realistic missing data assumptions.