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Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Deathâ€

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  • Junni L. Zhang
  • Donald B. Rubin

Abstract

The topic of “truncation by death†in randomized experiments arises in many fields, such as medicine, economics and education. Traditional approaches addressing this issue ignore the fact that the outcome after the truncation is neither “censored†nor “missing,†but should be treated as being defined on an extended sample space. Using an educational example to illustrate, we will outline here a formulation for tackling this issue, where we call the outcome “truncated by death†because there is no hidden value of the outcome variable masked by the truncating event. We first formulate the principal stratification ( Frangakis & Rubin, 2002 ) approach, and we then derive large sample bounds for causal effects within the principal strata, with or without various identification assumptions. Extensions are then briefly discussed.

Suggested Citation

  • Junni L. Zhang & Donald B. Rubin, 2003. "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Deathâ€," Journal of Educational and Behavioral Statistics, , vol. 28(4), pages 353-368, December.
  • Handle: RePEc:sae:jedbes:v:28:y:2003:i:4:p:353-368
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    Citations

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    Cited by:

    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. 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.
    3. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar Wüthrich, 2016. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Diskussionsschriften dp1607, Universitaet Bern, Departement Volkswirtschaft.
    4. 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.
    5. Zhichao Jiang & Peng Ding & Zhi Geng, 2016. "Principal causal effect identification and surrogate end point evaluation by multiple trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 829-848, September.
    6. repec:bla:biomet:v:74:y:2018:i:4:p:1232-1239 is not listed on IDEAS
    7. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    8. repec:zbw:espost:202031 is not listed on IDEAS
    9. Dustin M. Long & Michael G. Hudgens, 2013. "Sharpening Bounds on Principal Effects with Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 812-819, December.
    10. Alessandra Mattei & Fabrizia Mealli & Barbara Pacini, 2014. "Identification of causal effects in the presence of nonignorable missing outcome values," Biometrics, The International Biometric Society, vol. 70(2), pages 278-288, June.
    11. repec:bla:jorssb:v:79:y:2017:i:3:p:719-735 is not listed on IDEAS
    12. Mengling Liu & Zhiliang Ying, 2007. "Joint Analysis of Longitudinal Data with Informative Right Censoring," Biometrics, The International Biometric Society, vol. 63(2), pages 363-371, June.
    13. repec:bla:jorssb:v:79:y:2017:i:3:p:757-777 is not listed on IDEAS
    14. 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.
    15. Wei Yan & Yaqin Hu & Zhi Geng, 2012. "Identifiability of Causal Effects for Binary Variables with Baseline Data Missing Due to Death," Biometrics, The International Biometric Society, vol. 68(1), pages 121-128, March.
    16. 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.
    17. repec:bla:jorssa:v:180:y:2017:i:3:p:817-839 is not listed on IDEAS
    18. Debashis Ghosh, 2009. "On Assessing Surrogacy in a Single Trial Setting Using a Semicompeting Risks Paradigm," Biometrics, The International Biometric Society, vol. 65(2), pages 521-529, June.
    19. Murard, Elie, 2019. "The Impact of Migration on Family Left Behind: Estimation in Presence of Intra-Household Selection of Migrants," IZA Discussion Papers 12094, Institute of Labor Economics (IZA).
    20. Leandro de Magalhaes & Salomo Hirvonen, 2019. "The Incumbent-Challenger Advantage and the Winner-Runner-up Advantage," Bristol Economics Discussion Papers 19/710, Department of Economics, University of Bristol, UK.
    21. 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.
    22. Debashis Ghosh & Jeremy M. G. Taylor & Daniel J. Sargent, 2012. "Rejoinder for “Meta-analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling”," Biometrics, The International Biometric Society, vol. 68(1), pages 245-247, March.
    23. 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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