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Published on: September 5, 2018
A refreshing account of principal stratification
Fabrizia Mealli1, Alessandra Mattei
1University of Florence, Florence, Italy.
The International Journal of Biostatistics
|May 23, 2012
Summary
Principal stratification is a valuable framework in causal inference for analyzing intermediate outcomes. It offers distinct insights compared to mediation analysis and aids in assessing assumptions, especially with surrogate outcomes.
Area of Science:
- Causal Inference
- Statistical Methodology
- Biostatistics
Background:
- Principal stratification is a framework for causal inference.
- It is particularly relevant for causal estimands defined via intermediate outcomes.
- Discussion is invited on its logic and utility.
Purpose of the Study:
- To discuss the role and utility of principal stratification in causal inference.
- To differentiate principal stratification from mediation analysis.
- To explore its application in assessing identifying assumptions and surrogate outcomes.
Main Methods:
- Comparative analysis of principal stratification and mediation analysis.
- Discussion of the application of principal stratification to intermediate and surrogate outcomes.
- Emphasis on accounting for all principal strata and potential outcomes.
Main Results:
- Principal stratification and mediation analysis address different causal questions and estimands.
- Principal stratification can be a preliminary step to assess assumption plausibility.
- It is useful for analyzing causal effects related to intermediate or surrogate outcomes.
Conclusions:
- Principal stratification offers a distinct and useful approach in causal inference, especially for intermediate outcomes.
- Bayesian analysis is well-suited for principal stratification inference.
- The framework aids in understanding complex causal relationships and assumptions.
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