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Summary
This summary is machine-generated.

Reference-based multiple imputation handles missing data in clinical trials for longitudinal binary outcomes. A latent normal model approach is preferred for its reduced bias and information-anchored inference, especially with rarer outcomes.

Keywords:
binary outcomeclinical trialinformation anchoredreference‐based multiple imputationtreatment policy

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Longitudinal Data Analysis

Background:

  • Treatment policy strategies are common in clinical trials but complicated by missing data after treatment deviation.
  • Reference-based multiple imputation (MI) is established for continuous longitudinal outcomes.
  • Optimal implementation of reference-based MI for longitudinal binary data remains unclear.

Purpose of the Study:

  • To develop and compare algorithms for reference-based multiple imputation for longitudinal binary outcomes.
  • To evaluate the performance of two joint modeling approaches: multivariate normal distribution with adaptive rounding and a latent multivariate normal model.
  • To assess bias and information anchoring properties of the proposed methods.

Main Methods:

  • Formulated two algorithms for reference-based MI using joint modeling.
  • Algorithm 1: Multivariate normal distribution with adaptive rounding.
  • Algorithm 2: Latent multivariate normal model. Conducted a simulation study to compare methods.

Main Results:

  • Both methods provided approximately information-anchored inference across evaluated scenarios.
  • The latent normal approach generally yielded less bias, particularly for rarer outcomes.
  • Performance may be unsatisfactory for very rare outcomes ( ).

Conclusions:

  • Reference-based multiple imputation is a practical, information-anchored tool for treatment effect estimation with longitudinal binary outcomes under a treatment policy.
  • The latent multivariate normal model is the recommended implementation for its superior performance.
  • Careful consideration is needed for very rare outcomes.