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An R-Based Landscape Validation of a Competing Risk Model
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Causal interactions in the proportional hazards model.

Tyler J VanderWeele1

  • 1Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. tvanderw@hsph.harvard.edu

Epidemiology (Cambridge, Mass.)
|May 12, 2011
PubMed
Summary

This study links proportional hazards models to causal interactions using a counterfactual framework. It shows that causal interactions for time-to-event data can change over time, depending on follow-up duration.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Survival Analysis

Background:

  • Additive interaction in proportional hazards models is commonly used in survival analysis.
  • Causal interaction is a key concept in establishing causal relationships.
  • Existing definitions of causal interaction are primarily for dichotomous outcomes.

Purpose of the Study:

  • To define and estimate causal interaction for time-to-event outcomes.
  • To generalize existing definitions of causal interaction to time-to-event data.
  • To investigate the relationship between statistical interaction in proportional hazards models and causal interaction.

Main Methods:

  • Utilized the counterfactual framework to define causal interaction for time-to-event outcomes.
  • Developed conditions based on relative excess risk due to interaction in proportional hazards models.
  • Assessed the time-dependent nature of causal interactions and derived bounds on their prevalence.

Main Results:

  • Provided a novel definition of causal interaction for time-to-event data.
  • Established conditions under which statistical interaction implies causal interaction.
  • Demonstrated that causal interactions can emerge or disappear over time, depending on follow-up.
  • Derived methods to assess the time range and baseline survival probabilities for which causal interactions are present.

Conclusions:

  • Causal interaction in time-to-event analysis is time-dependent, a unique feature compared to dichotomous outcomes.
  • The proposed framework allows for a more nuanced understanding of interaction effects in survival data.
  • The findings have implications for interpreting interaction terms in proportional hazards models and for causal inference in longitudinal studies.