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Comments on 'standard and reference-based conditional mean imputation': Regulators and trial statisticians be aware!

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Summary

Reference-based imputation methods in clinical trials can yield smaller variance estimates with more missing data, inappropriately forcing treatment effects to zero. This challenges their use in confirmatory trials.

Keywords:
clinical trialconditional mean imputationestimandsmissing datamultiple imputationreference‐based imputationtreatment policyvariance estimation

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Area of Science:

  • Statistics
  • Clinical Trials
  • Biostatistics

Background:

  • Confirmatory clinical trials require accurate frequentist performance for missing data methods.
  • Reference-based conditional mean imputation is one such method evaluated for its statistical properties.

Discussion:

  • This imputation method exhibits an undesirable property where increased missing observations lead to decreased variance estimates.
  • This occurs because the method effectively assumes a zero treatment effect for patients with missing data, a phenomenon termed 'jump-to-reference'.

Key Insights:

  • Frequentist performance alone is insufficient justification for employing a missing data method in clinical trials.
  • The 'jump-to-reference' issue in conditional mean imputation distorts treatment effect estimation by driving it towards zero as data becomes more missing.

Outlook:

  • Further research is needed to develop robust missing data methods that do not suffer from the 'jump-to-reference' bias.
  • Alternative imputation strategies should be explored to ensure valid and reliable statistical inference in the presence of missing data.