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Sensitivity Analyses for Missing in Repeatedly Measured Outcome Data.

James F Troendle1, Aparajita Sur2, Eric S Leifer1

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Statistics in Medicine
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Summary
This summary is machine-generated.

This study introduces delta-based imputation methods for sensitivity analyses in trials with missing outcome data. Using multiple imputation by chained equations (MICE) and last-observed covariates improves analysis for repeated measures.

Keywords:
delta‐based controlled imputationlinear mixed modelmultiple imputation

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Data Science

Background:

  • Missing data in clinical trials, especially with repeatedly measured outcomes, poses significant challenges for robust statistical analysis.
  • Sensitivity analyses are crucial for evaluating the potential impact of missing data on trial conclusions.
  • Sequential, Multiple, Assignment, Randomized Trials (SMART) often involve complex data structures and missingness patterns.

Purpose of the Study:

  • To present practical methods for conducting sensitivity analyses for missing data in clinical trials with repeatedly measured outcomes.
  • To adapt and enhance delta-based imputation approaches for use with linear mixed models, common in longitudinal trial analyses.
  • To introduce novel metrics for assessing the adequacy of these sensitivity analyses.

Main Methods:

  • Discussed delta-based controlled imputation strategies tailored for sensitivity analyses.
  • Employed Multiple Imputation by Chained Equations (MICE) to enhance imputation quality for sensitivity analyses.
  • Incorporated last-observed-before-time covariates within the imputation models for repeatedly measured outcomes.

Main Results:

  • Delta-based sensitivity analyses are demonstrably enhanced by using MICE for imputation in trials with repeatedly measured outcomes.
  • The inclusion of last-observed-before-time covariates is identified as a critical factor for accurate sensitivity analyses in longitudinal data.
  • Novel metrics were developed to quantitatively assess the sufficiency and reliability of the conducted sensitivity analyses.

Conclusions:

  • The proposed delta-based imputation methods, particularly when combined with MICE and appropriate covariates, provide a robust framework for sensitivity analyses in longitudinal trials with missing data.
  • These methods aid researchers in understanding the potential impact of missing outcome data on trial results, thereby increasing confidence in study conclusions.
  • The developed metrics offer a standardized approach to evaluating the quality of sensitivity analyses, promoting transparency and rigor in clinical trial reporting.