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Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random: A high

Niko A Kaciroti1,2, Roderick J A Little1

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Statistics in Medicine
|August 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian pattern-mixture model to analyze longitudinal data with missing outcomes, addressing potential bias from missing not at random (MNAR) mechanisms. The novel approach allows for sensitivity analysis, improving the reliability of clinical trial results.

Keywords:
MNAR future dependentTROPHY trialclinical trialshypertensionmissing datatipping point analysis

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Longitudinal Data Analysis

Background:

  • Longitudinal randomized clinical trials often use random effects or GEE models.
  • These models typically assume data are missing at random (MAR) or missing completely at random (MCAR).
  • This assumption can lead to biased results if data are missing not at random (MNAR).

Purpose of the Study:

  • To propose a Bayesian pattern-mixture model to handle MNAR data in longitudinal studies.
  • To conduct sensitivity analyses to assess the impact of different missingness mechanisms.
  • To evaluate deviations from the MAR assumption.

Main Methods:

  • Developed a Bayesian pattern-mixture model accommodating MNAR.
  • Incorporated sensitivity parameters relating missing and observed data distributions.
  • Reduced sensitivity parameter dimensionality using prior distributions.
  • Applied the model to data from the Trial of Preventing Hypertension.

Main Results:

  • The proposed model can incorporate MNAR mechanisms, unlike standard methods.
  • Sensitivity analysis quantifies the impact of deviations from MAR.
  • The approach was successfully applied to real-world clinical trial data.

Conclusions:

  • The Bayesian pattern-mixture model offers a robust framework for analyzing longitudinal data with potential MNAR.
  • This method enhances the reliability of findings from randomized clinical trials by accounting for complex missingness.
  • It provides a valuable tool for sensitivity analysis, moving beyond MAR assumptions.