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Commentary on "Principal Stratification -- a Goal or a Tool?" by Judea Pearl

Author

Listed:
  • Gilbert Peter B.

    (Fred Hutchinson Cancer Research Center & University of Washington)

  • Hudgens Michael G.

    (University of North Carolina at Chapel Hill)

  • Wolfson Julian

    (University of Minnesota, Twin Cities)

Abstract

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.

Suggested Citation

  • Gilbert Peter B. & Hudgens Michael G. & Wolfson Julian, 2011. "Commentary on "Principal Stratification -- a Goal or a Tool?" by Judea Pearl," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-15, September.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:36
    DOI: 10.2202/1557-4679.1341
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    References listed on IDEAS

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    1. Yannis Jemiai & Andrea Rotnitzky & Bryan E. Shepherd & Peter B. Gilbert, 2007. "Semiparametric estimation of treatment effects given base‐line covariates on an outcome measured after a post‐randomization event occurs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 879-901, November.
    2. Hudgens, Michael G. & Halloran, M. Elizabeth, 2006. "Causal Vaccine Effects on Binary Postinfection Outcomes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 51-64, March.
    3. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
    4. Dean Follmann, 2006. "Augmented Designs to Assess Immune Response in Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(4), pages 1161-1169, December.
    5. Peter B. Gilbert & Michael G. Hudgens, 2008. "Evaluating Candidate Principal Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 64(4), pages 1146-1154, December.
    6. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    7. Peter B. Gilbert & Ronald J. Bosch & Michael G. Hudgens, 2003. "Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 531-541, September.
    8. Marshall M. Joffe & Tom Greene, 2009. "Related Causal Frameworks for Surrogate Outcomes," Biometrics, The International Biometric Society, vol. 65(2), pages 530-538, June.
    9. Bryan E. Shepherd & Peter B. Gilbert & Charles T. Dupont, 2011. "Sensitivity Analyses Comparing Time-to-Event Outcomes Only Existing in a Subset Selected Postrandomization and Relaxing Monotonicity," Biometrics, The International Biometric Society, vol. 67(3), pages 1100-1110, September.
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    Cited by:

    1. Gilbert Peter B. & Huang Ying & Gabriel Erin E. & Chan Ivan S.F., 2015. "Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition," Journal of Causal Inference, De Gruyter, vol. 3(2), pages 157-175, September.
    2. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    3. Ali Tafti & Galit Shmueli, 2020. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure," Information Systems Research, INFORMS, vol. 31(4), pages 1183-1199, December.
    4. Nima S. Hejazi & Mark J. van der Laan & Holly E. Janes & Peter B. Gilbert & David C. Benkeser, 2021. "Efficient nonparametric inference on the effects of stochastic interventions under two‐phase sampling, with applications to vaccine efficacy trials," Biometrics, The International Biometric Society, vol. 77(4), pages 1241-1253, December.

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