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On Estimation of the Survivor Average Causal Effect in Observational Studies When Important Confounders Are Missing Due to Death

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  • Brian L. Egleston
  • Daniel O. Scharfstein
  • Ellen MacKenzie

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  • Brian L. Egleston & Daniel O. Scharfstein & Ellen MacKenzie, 2009. "On Estimation of the Survivor Average Causal Effect in Observational Studies When Important Confounders Are Missing Due to Death," Biometrics, The International Biometric Society, vol. 65(2), pages 497-504, June.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:2:p:497-504
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01111.x
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    References listed on IDEAS

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    1. Rick L. Williams, 2000. "A Note on Robust Variance Estimation for Cluster-Correlated Data," Biometrics, The International Biometric Society, vol. 56(2), pages 645-646, June.
    2. 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.
    3. 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.
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    Cited by:

    1. Paul R. Rosenbaum, 2013. "Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies," Biometrics, The International Biometric Society, vol. 69(1), pages 118-127, March.
    2. Anna M. Wilke & Donald P. Green & Jasper Cooper, 2020. "A placebo design to detect spillovers from an education–entertainment experiment in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1075-1096, June.
    3. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
    4. 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.
    5. Paul R. Rosenbaum & Dylan S. Small, 2017. "An adaptive Mantel–Haenszel test for sensitivity analysis in observational studies," Biometrics, The International Biometric Society, vol. 73(2), pages 422-430, June.
    6. Colin B. Fogarty, 2023. "Testing weak nulls in matched observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2196-2207, September.
    7. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    8. Greene Tom & Joffe Marshall & Hu Bo & Li Liang & Boucher Ken, 2013. "The Balanced Survivor Average Causal Effect," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 291-306, May.
    9. Yi He & Linzhi Zheng & Peng Luo, 2023. "Treatment Benefit and Treatment Harm Rates with Nonignorable Missing Covariate, Endpoint, or Treatment," Mathematics, MDPI, vol. 11(21), pages 1-18, October.
    10. Ting Dai & Adam Davey, 2023. "Determining Dimensionality with Dichotomous Variables: A Monte Carlo Simulation Study and Applications to Missing Data in Longitudinal Research," Mathematics, MDPI, vol. 11(6), pages 1-25, March.

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