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Summary

This study introduces tripartite estimands to analyze clinical trial data, addressing intercurrent events and missing values. These estimands provide a comprehensive understanding of treatment effects, including adverse events, lack of efficacy, and adherence to treatment.

Keywords:
ICH E9 (R1)adherenceintercurrent eventsmissing dataprincipal stratification

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Area of Science:

  • Clinical Trials Methodology
  • Causal Inference
  • Biostatistics

Background:

  • Intercurrent events (ICEs) and missing data complicate treatment effect assessment in clinical trials.
  • Standard analyses may not fully capture the nuances of treatment outcomes when ICEs occur.
  • Defining relevant estimands is crucial for meaningful data analysis.

Purpose of the Study:

  • To detail the estimation and interpretation of tripartite estimands within a causal inference framework.
  • To provide a method for understanding the totality of treatment effects, considering various scenarios of ICEs.
  • To illustrate the application of tripartite estimands in a real-world clinical trial setting.

Main Methods:

  • Utilizing a causal inference framework to define and estimate tripartite estimands.
  • The tripartite estimands assess treatment differences in: proportion with ICEs due to adverse events, proportion with ICEs due to lack of efficacy, and primary outcome for treatment adherents.
  • Application demonstrated in a Phase 3 basal insulin trial for type 1 diabetes.

Main Results:

  • The manuscript provides a methodological discussion on estimating tripartite estimands.
  • Interpretation guidelines for tripartite estimates are presented.
  • The approach offers a more complete picture of treatment effects compared to traditional methods.

Conclusions:

  • Tripartite estimands offer a robust framework for analyzing clinical trial data with intercurrent events and missing values.
  • This method enhances the understanding of treatment effects for diverse patient outcomes.
  • The causal inference approach provides valuable insights for stakeholders in clinical research.