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A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data.

Daniel Tompsett1, Stephen Sutton2, Shaun R Seaman3

  • 1Great Ormond Street Institute of Child Health, UCL, London, UK.

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|July 18, 2020
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Summary
This summary is machine-generated.

This study introduces methods for sensitivity analyses in incomplete repeated binary outcome data. Conclusions on smoking cessation interventions were largely insensitive to missing data assumptions, except under extreme non-responder behavior differences.

Keywords:
MARMNARexpert elicitationmultiple imputationsmoking cessation

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Data Analysis

Background:

  • Incomplete binary outcome data in clinical trials pose challenges for robust statistical inference.
  • The missing at random (MAR) assumption is often untestable and may not hold in practice.
  • Sensitivity analyses are crucial to evaluate the impact of potential departures from MAR.

Purpose of the Study:

  • To develop and demonstrate methods for sensitivity analyses assessing departures from MAR in incomplete repeated binary outcome data.
  • To utilize expert opinion for quantifying uncertainty in sensitivity parameters (SPs).
  • To evaluate the robustness of conclusions from a smoking cessation trial under various MAR departure scenarios.

Main Methods:

  • Employed multiple imputation within a not at random fully conditional specification framework.
  • Incorporated sensitivity parameters (SPs) for each incomplete variable.
  • Used an online elicitation questionnaire and highest prior density regions for expert opinion pooling on SPs.

Main Results:

  • Substantive conclusions can be highly sensitive to MAR departures when non-responders in control and intervention groups differ.
  • The correlation between arm-specific SPs, informed by expert opinion, is particularly influential.
  • Conclusions regarding the iQuit in Practice smoking cessation trial's intervention effect were broadly insensitive to MAR departures within realistic bounds.

Conclusions:

  • The developed methods provide a framework for assessing the impact of missing data assumptions in clinical trials.
  • Expert opinion can be effectively integrated to inform sensitivity analyses.
  • The iQuit in Practice trial's findings on smoking cessation are robust to plausible departures from the MAR assumption.