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Most Downloaded Articles
- Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems by Díaz, Iván and van der Laan, Mark J.
- An Introduction to Causal Inference by Pearl, Judea
- Principal Stratification: A Broader Vision by Shrier, Ian/ Kaufman, Jay S./ Platt, Robert W. and Steele, Russell J.
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions by Díaz, Iván and van der Laan, Mark J.
Commentary on "Principal Stratification -- a Goal or a Tool?" by Judea Pearl
1Fred Hutchinson Cancer Research Center & University of Washington
2University of North Carolina at Chapel Hill
3University of Minnesota, Twin Cities
Citation Information: The International Journal of Biostatistics. Volume 7, Issue 1, Pages 1–15, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1341, September 2011
- Published Online:
This commentary takes up Pearl's welcome challenge to clearly articulate the scientific value of principal stratification estimands that we and colleagues have investigated, in the area of randomized placebo-controlled preventive vaccine efficacy trials, especially trials of HIV vaccines. After briefly arguing that certain principal stratification estimands for studying vaccine effects on post-infection outcomes are of genuine scientific interest, the bulk of our commentary argues that the “causal effect predictiveness” (CEP) principal stratification estimand for evaluating immune biomarkers as surrogate endpoints is not of ultimate scientific interest, because it evaluates surrogacy restricted to the setting of a particular vaccine efficacy trial, but is nevertheless useful for guiding the selection of primary immune biomarker endpoints in Phase I/II vaccine trials and for facilitating assessment of transportability/bridging surrogacy.