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A generalized theory of separable effects in competing event settings
Mats J Stensrud1, Miguel A Hernán2,3,4, Eric J Tchetgen Tchetgen5
1Department of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. mats.stensrud@epfl.ch.
This study generalizes separable effects to interpret treatment impacts in complex competing event scenarios. It provides new methods for identifying and estimating these effects, even with time-varying factors and unobserved confounders.
Area of Science:
- Causal Inference
- Epidemiology
- Biostatistics
Background:
- Interpreting treatment effects in competing event settings is complex due to indirect contributions.
- Previous definitions of separable effects were limited to simpler causal structures.
Purpose of the Study:
- To generalize the concept of separable effects for broader applicability in competing risks.
- To enable interpretation, identification, and estimation of treatment effects in complex, time-varying settings.
Main Methods:
- Proposed a generalized definition of separable effects applicable to general time-varying structures.
- Derived weaker identification conditions, accommodating time-varying common causes.
- Developed semi-parametric weighted estimators for practical implementation.
Main Results:
- Separable effects can be interpreted as modified treatment effects, even when not strictly direct/indirect.
- Identification conditions are relaxed, allowing for more realistic study designs.
- Proposed estimators are straightforward to implement.
Conclusions:
- The generalized separable effects framework offers a more flexible approach to causal effect decomposition in competing risks.
- This approach allows for empirical scrutiny in future research, advancing causal inference methodology.
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