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Information-anchored sensitivity analysis: theory and application.

Suzie Cro1, James R Carpenter2, Michael G Kenward3

  • 1University College London London School of Hygiene and Tropical Medicine, and Imperial College London UK.

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|March 5, 2019
PubMed
Summary
This summary is machine-generated.

Protocol deviations in clinical trials complicate analysis. Information-anchored sensitivity analysis ensures consistent information loss from missing data, providing a transparent approach for robust treatment effect estimation.

Keywords:
Controlled multiple imputationDeviationsMissing dataRandomized controlled trialSensitivity analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Longitudinal Data Analysis

Background:

  • Protocol deviations in longitudinal randomized clinical trials complicate primary analysis.
  • Estimating treatment effects requires untestable assumptions about post-deviation behavior.
  • Sensitivity analyses are crucial for assessing robustness but often neglect appropriate handling of missing data.

Purpose of the Study:

  • To address the issue of appropriate information loss due to missing data within sensitivity analyses.
  • To introduce and define the concept of information-anchored sensitivity analysis.
  • To provide a transparent and practical starting point for interpreting sensitivity analyses.

Main Methods:

  • Introduction of the 'information-anchored' sensitivity analysis concept.
  • Derivation of theoretical results for longitudinal continuous data.
  • Illustration using simulations and a peer review trial analysis.

Main Results:

  • Sensitivity analyses can paradoxically increase or decrease information about treatment effects.
  • Information-anchored sensitivity analysis maintains consistent information loss proportions.
  • Controlled and reference-based sensitivity analyses using multiple imputation are shown to be information-anchored for longitudinal continuous data.

Conclusions:

  • Information-anchored sensitivity analysis offers a principled approach to handling missing data within sensitivity analyses.
  • This method provides a transparent basis for interpreting the robustness of clinical trial inferences.
  • Theoretical results support the use of controlled multiple-imputation procedures for sensitivity analysis in longitudinal studies.