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A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized

Mallorie H Fiero1, Chiu-Hsieh Hsu2, Melanie L Bell2

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Statistics in Medicine
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This summary is machine-generated.

This study introduces a new method for handling missing data in longitudinal cluster randomized trials. The approach uses multilevel multiple imputation and a sensitivity parameter to assess how missing data assumptions affect results.

Keywords:
cluster randomized trialsmissing datamultiple imputationpattern-mixture model

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Longitudinal Data Analysis

Background:

  • Longitudinal cluster randomized trials (CRTs) present unique challenges for handling missing outcome data due to their hierarchical structure.
  • Existing methods may not adequately account for the complex dependencies within clustered data when dealing with dropouts.

Purpose of the Study:

  • To extend the pattern-mixture approach for analyzing continuous missing outcome data in longitudinal CRTs.
  • To develop a robust method for assessing the impact of missing data assumptions on treatment effect estimates in CRTs.

Main Methods:

  • Utilized a pattern-mixture modeling framework combined with multilevel multiple imputation.
  • Introduced a sensitivity parameter (k) to extrapolate pattern-mixture models under various missing data scenarios.
  • Applied the method to both simulated and real-world data from a cluster randomized trial.

Main Results:

  • Simulated data demonstrated that parameter estimates and treatment effect inferences can significantly vary based on different missing data assumptions.
  • Sensitivity analysis on real CRT data showed how increasing the sensitivity parameter (k) impacts treatment effect conclusions.
  • The proposed method effectively highlights the potential influence of missing data on study findings.

Conclusions:

  • The extended pattern-mixture approach with multilevel multiple imputation provides a valuable tool for addressing missing continuous outcomes in longitudinal CRTs.
  • Performing sensitivity analyses is crucial for researchers to evaluate the plausibility of missing data assumptions and their impact on study results.
  • This methodology enhances the transparency and reliability of findings from cluster randomized trials with missing data.