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Inference for Cumulative Incidences and Treatment Effects in Randomized Controlled Trials With Time-to-Event Outcomes

Yuhao Deng1, Shasha Han2, Xiao-Hua Zhou3,4

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Statistics in Medicine
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Summary
This summary is machine-generated.

This study presents methods for analyzing time-to-event outcomes in randomized controlled trials (RCTs) with intercurrent events, focusing on causal interpretations. It offers practical approaches for defining, estimating, and inferring causal effects in complex clinical trial data.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Epidemiology

Background:

  • Intercurrent events in time-to-event analyses of randomized controlled trials (RCTs) pose challenges, acting as semi-competing or competing events.
  • Existing strategies in ICH E9 (R1) addendum are not readily applicable for causal inference with time-to-event outcomes.
  • Addressing these events is crucial for accurate interpretation of treatment effects in clinical research.

Purpose of the Study:

  • To provide a framework for defining, estimating, and making inferences about causal objectives in RCTs with intercurrent events.
  • To adapt existing strategies for causal interpretation in time-to-event outcome settings.
  • To introduce novel statistical methods for handling semi-competing and competing events in clinical trials.

Main Methods:

  • Derivation of mathematical formulations for causal estimands corresponding to the five ICH E9 (R1) strategies.
  • Clarification of necessary data structures for identifying these causal estimands.
  • Introduction of nonparametric estimation methods, including asymptotic variance and hypothesis testing for causal estimands.

Main Results:

  • Established clear mathematical definitions for causal estimands in the presence of intercurrent events.
  • Developed and validated nonparametric methods for estimating and performing inference on these causal estimands.
  • Demonstrated the practical application of the proposed methods using data from the LEADER Trial.

Conclusions:

  • The proposed methods enable robust causal inference for time-to-event outcomes affected by intercurrent events in RCTs.
  • The study provides a valuable toolkit for biostatisticians and clinical researchers dealing with complex event data.
  • Application to the LEADER Trial highlights the utility of these methods in real-world cardiovascular outcome studies.