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The Large Sample Bounds on the Principal Strata Effect with Application to a Prostate Cancer Prevention Trial

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  • Chiba Yasutaka

    (Kinki University School of Medicine)

Abstract

Issues of post-randomization selection bias and truncation-by-death can arise in randomized clinical trials; for example, in a cancer prevention trial, an outcome such as cancer severity is undefined for individuals who do not develop cancer. Restricting analysis to a subpopulation selected after randomization can give rise to biased outcome comparisons. One approach to deal with such issues is to consider the principal strata effect (PSE, or equally, the survivor average causal effect). PSE is defined as the effect of treatment on the outcome among the subpopulation that would have been selected under either treatment arm. Unfortunately, the PSE cannot generally be estimated without the identifying assumptions; however, the bounds can be derived using a deterministic causal model. In this paper, we propose a number of assumptions for deriving the bounds with narrow width. The assumptions and bounds, which differ from those introduced by Zhang and Rubin (2003), are illustrated using data from a randomized prostate cancer prevention trial.

Suggested Citation

  • Chiba Yasutaka, 2012. "The Large Sample Bounds on the Principal Strata Effect with Application to a Prostate Cancer Prevention Trial," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, May.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:12
    DOI: 10.1515/1557-4679.1365
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    References listed on IDEAS

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    1. Constantine E. Frangakis & Donald B. Rubin & Ming-Wen An & Ellen MacKenzie, 2007. "Principal Stratification Designs to Estimate Input Data Missing Due to Death," Biometrics, The International Biometric Society, vol. 63(3), pages 641-649, September.
    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. Bryan E. Shepherd & Peter B. Gilbert & Yannis Jemiai & Andrea Rotnitzky, 2006. "Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(2), pages 332-342, June.
    4. Shepherd, Bryan E. & Redman, Mary W. & Ankerst, Donna P., 2008. "Does Finasteride Affect the Severity of Prostate Cancer? A Causal Sensitivity Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1392-1404.
    5. Arvid Sjölander & Keith Humphreys & Stijn Vansteelandt & Rino Bellocco & Juni Palmgren, 2009. "Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease," Biometrics, The International Biometric Society, vol. 65(2), pages 514-520, June.
    6. A. Mattei & F. Mealli, 2007. "Application of the Principal Stratification Approach to the Faenza Randomized Experiment on Breast Self-Examination," Biometrics, The International Biometric Society, vol. 63(2), pages 437-446, June.
    7. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    8. 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.
    9. Douglas Hayden & Donna K. Pauler & David Schoenfeld, 2005. "An Estimator for Treatment Comparisons among Survivors in Randomized Trials," Biometrics, The International Biometric Society, vol. 61(1), pages 305-310, March.
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    Cited by:

    1. Gilbert Peter B. & Blette Bryan S. & Hudgens Michael G. & Shepherd Bryan E., 2020. "Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 54-69, January.
    2. Gilbert Peter B. & Blette Bryan S. & Hudgens Michael G. & Shepherd Bryan E., 2020. "Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 54-69, January.
    3. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.

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