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A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome.

Sally Hollis1

  • 1Medical Statistics Unit, Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster LA1 4YF, UK. s.hollis@lancs.ac.uk

Statistics in Medicine
|December 17, 2002
PubMed
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Many clinical trials fail to fully apply the intention-to-treat (ITT) approach due to missing data. This study proposes a graphical sensitivity analysis to assess the impact of missing outcome data in ITT analyses.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Intention-to-treat (ITT) analysis is a standard for clinical trials, requiring complete outcome data for all randomized subjects.
  • A survey revealed common use of complete case analysis in ITT, violating ITT principles by excluding subjects with missing data.
  • Analyses with missing data rely on untestable assumptions, necessitating sensitivity analyses.

Purpose of the Study:

  • To address the challenge of missing outcome data in intention-to-treat (ITT) analyses.
  • To propose a novel graphical sensitivity analysis method for binary outcomes.
  • To provide a tool for assessing the impact of missing data under various assumptions.

Main Methods:

  • Review of current practices in ITT analysis and handling of missing data.

Related Experiment Videos

  • Development of a graphical sensitivity analysis for binary outcomes.
  • Extension of the graphical method to incorporate binomial variation.

    • Main Results:

    • Complete case analysis is prevalent but non-compliant with ITT principles.
    • A graphical method is proposed to visualize results across all possible missing data allocations.
    • The method allows for informal examination of sensitivity to prior beliefs about missing data.

    Conclusions:

    • Full intention-to-treat (ITT) analysis is compromised by missing outcome data.
    • The proposed graphical sensitivity analysis offers a more comprehensive approach to evaluating missing data impact.
    • This method aids in understanding the robustness of ITT findings under different missing data scenarios.