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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
    DOI: 10.3102/10769986028004353
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    Cited by:

    1. 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.
    2. 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.
    3. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly & Kaspar Wüthrich, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 123-137, January.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Leandro de Magalhaes & Salomo Hirvonen, 2019. "The Incumbent-Challenger Advantage and the Winner-Runner-up Advantage," Bristol Economics Discussion Papers 19/710, School of Economics, University of Bristol, UK.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Fan Yang & Peng Ding, 2018. "Using survival information in truncation by death problems without the monotonicity assumption," Biometrics, The International Biometric Society, vol. 74(4), pages 1232-1239, December.
    15. Blair, Graeme & Cooper, Jasper & Coppock, Alexander & Humphreys, Macartan, 2019. "Declaring and Diagnosing Research Designs," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 113(3), pages 838-859.
    16. Linbo Wang & Thomas S. Richardson & Xiao-Hua Zhou, 2017. "Causal analysis of ordinal treatments and binary outcomes under truncation by death," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 719-735, June.
    17. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    18. 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.
    19. 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.
    20. Myoung-jae Lee, 2017. "Extensive and intensive margin effects in sample selection models: racial effects on wages," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 817-839, June.
    21. 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).
    22. 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.
    23. Nobles, Jenna & Hamoudi, Amar, 2019. "Detecting the Effects of Early-Life Exposures: Why Fecundity Matters," SocArXiv x4zm6, Center for Open Science.
    24. Jenna Nobles & Amar Hamoudi, 2019. "Detecting the Effects of Early-Life Exposures: Why Fecundity Matters," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 38(6), pages 783-809, December.
    25. 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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