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Missing binary outcomes under covariate-dependent missingness in cluster randomised trials.

Anower Hossain1,2, Karla DiazOrdaz1, Jonathan W Bartlett3

  • 1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.

Statistics in Medicine
|May 31, 2017
PubMed
Summary
This summary is machine-generated.

Handling missing outcomes in cluster randomized trials is crucial. This study evaluates methods like cluster-level and individual-level analyses, providing guidance for valid inference when outcomes are missing.

Keywords:
baseline covariate-dependent missingnesscluster randomised trialscomplete records analysismissing binary outcomemultiple imputation

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Missing outcomes are common in cluster randomized trials (CRTs).
  • Ignoring missing data can lead to biased and inefficient statistical inference.
  • Appropriate methods are needed to handle missing outcomes in CRTs.

Purpose of the Study:

  • To assess the performance of different analytical approaches for CRTs with missing binary outcomes.
  • To evaluate methods under a baseline covariate-dependent missingness mechanism.
  • To provide guidance on selecting valid analytical methods based on study conditions.

Main Methods:

  • Compared unadjusted cluster-level analysis, covariate-adjusted cluster-level analysis, random effects logistic regression, and generalized estimating equations.
  • Utilized complete records analysis and multilevel multiple imputation for handling missing outcomes.
  • Conducted analytical derivations and a simulation study with four scenarios.

Main Results:

  • Cluster-level analyses for risk ratio using complete records are valid under specific conditions (log link, same missingness mechanism, same covariate effect).
  • Simulation results identified scenarios where different methods perform better.
  • Guidance is provided on the validity of each approach based on missingness mechanisms and covariate interactions.

Conclusions:

  • The choice of method for analyzing CRTs with missing outcomes depends on the specific data-generating model and missingness characteristics.
  • Understanding the conditions for valid inference is essential for accurate results in CRTs.
  • This research offers practical recommendations for researchers dealing with missing outcome data in cluster randomized trials.