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Missing Data in Alcohol Clinical Trials with Binary Outcomes.

Kevin A Hallgren1, Katie Witkiewitz2, Henry R Kranzler3,4

  • 1Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington.

Alcoholism, Clinical and Experimental Research
|June 3, 2016
PubMed
Summary
This summary is machine-generated.

Handling missing data in alcohol clinical trials is crucial. Multiple imputation (MI) is recommended over complete case analysis (CCA) and worst case scenario (WCS) for binary outcomes to reduce bias.

Keywords:
Alcohol Clinical TrialsMissing DataMultiple ImputationNaltrexoneSimulation Study

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Addiction Medicine

Background:

  • Missing data are prevalent in alcohol clinical trials, affecting both continuous and binary outcomes.
  • Existing research on handling missing data primarily focuses on continuous outcomes, leaving a gap in understanding for binary endpoints.
  • This study addresses the need to compare different methods for managing missing binary outcome data within the COMBINE study.

Purpose of the Study:

  • To compare various statistical approaches for handling missing binary outcome data in alcohol clinical trials.
  • To evaluate the performance of complete case analysis (CCA), last observation carried forward (LOCF), worst case scenario (WCS), and multiple imputation (MI) for binary outcomes.
  • To identify the most reliable method for estimating treatment effects in the presence of missing data in the COMBINE study.

Main Methods:

  • The study analyzed data from 1,146 participants in the COMBINE study, assigned to active medication or placebo.
  • Simulations were used to introduce missing data under various scenarios of sample size and missingness.
  • Four analytic approaches—CCA, LOCF, WCS, and MI—were employed to estimate treatment effects on drinking outcomes, with additional analyses for participants who discontinued treatment.

Main Results:

  • Worst case scenario (WCS) analysis resulted in the most significant bias in treatment effect estimates.
  • Multiple imputation (MI) generally produced less biased estimates compared to WCS and CCA in simulated data.
  • MI demonstrated superior performance over LOCF when assessing treatment effects in participants who discontinued treatment.

Conclusions:

  • Missing data can introduce bias into treatment effect estimates in alcohol clinical trials.
  • Researchers are advised to adopt advanced missing data techniques like MI for binary outcomes.
  • Complete case analysis (CCA) and worst case scenario (WCS) should be avoided for analyzing binary alcohol clinical trial outcomes.