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|November 7, 2025
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Summary
This summary is machine-generated.

This study clarifies statistical properties of average treatment effect estimators in covariate-adaptive randomization with missing covariate data. It provides methods for valid inference, enhancing clinical trial analysis.

Keywords:
causal inferencecovariate‐adaptive randomizationmissing valuesregression adjustmentstyranny‐of‐the‐minority

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Covariate-adaptive randomization balances prognostic factors in clinical trials.
  • Regression adjustments improve estimation efficiency.
  • Missing covariate data poses challenges for analysis.

Purpose of the Study:

  • To investigate asymptotic properties of average treatment effect estimators with missing covariates under covariate-adaptive randomization.
  • To develop consistent variance estimators for valid statistical inference.
  • To evaluate finite-sample performance of different methods.

Main Methods:

  • Combining missing data handling procedures with regression adjustment.
  • Asymptotic analysis of treatment effect estimators.
  • Derivation of consistent variance estimators.
  • Model-free analysis ensuring validity under misspecification.

Main Results:

  • Established asymptotic properties for average treatment effect estimators.
  • Developed consistent variance estimators for reliable inference.
  • Numerical studies demonstrated performance across various scenarios.

Conclusions:

  • The proposed methods provide a robust framework for analyzing clinical trials with missing covariate data under covariate-adaptive randomization.
  • The model-free approach ensures validity even with potential regression model misspecification.
  • Recommendations are provided for practical application based on simulation results.