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Impute the missing data using retrieved dropouts.

Shuai Wang1, Haoyan Hu2

  • 1Global Product Development, Pfizer Inc, Groton, CT, 06340, USA. shuai1107@hotmail.com.

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|March 30, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new multiple imputation method for handling missing data in clinical trials, aligning with the "treatment policy" estimand. This approach uses retrieved dropouts under a more realistic missing not at random (MNAR) assumption, suitable for primary analysis.

Keywords:
ICH E9 (R1)Missing not at randomMultiple imputationRetrieved dropoutsTreatment policy

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Data Analysis

Background:

  • Handling missing data in clinical studies is crucial for result robustness.
  • Traditional methods often assume missing at random (MAR), which is unrealistic for treatment discontinuation.
  • Existing missing not at random (MNAR) methods can be overly conservative, hindering consensus.

Purpose of the Study:

  • To propose a novel multiple imputation method for clinical trials.
  • To address limitations of existing methods for missing data, particularly under MNAR assumptions.
  • To align with the "treatment policy" estimand as defined by ICH E9 (R1).

Main Methods:

  • Developed a multiple imputation technique utilizing "retrieved dropouts" (study participants who remain despite intercurrent events).
  • The method is based on a missing not at random (MNAR) assumption, considered more realistic than MAR.
  • Imputed data are analyzed alongside completers and retrieved dropouts, with results summarized into a single estimate.

Main Results:

  • The proposed approach demonstrates well-controlled type I error rates without compromising statistical power.
  • Effect size estimates accurately reflect the dilution effect from retrieved dropouts, consistent with the MNAR assumption.
  • The method provides a realistic and reasonable approach to handling missing data.

Conclusions:

  • This multiple imputation method can serve as a primary analysis, not just a sensitivity analysis.
  • It is particularly suitable for trials with adequate retrieved dropout data or those designed to collect such data.
  • The approach offers a more pragmatic solution for missing data in clinical research.