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The Balanced Survivor Average Causal Effect

Author

Listed:
  • Greene Tom

    () (Division of Epidemiology, Department of Internal Medicine, University of Utah)

  • Joffe Marshall

    () (Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, United States)

  • Hu Bo

    ()

  • Li Liang

    () (Department of Quantitative Health Sciences, Cleveland Clinic, OH, United States)

  • Boucher Ken

    () (Department of Oncological Sciences, University of Utah, UT, United States)

Abstract

Statistical analysis of longitudinal outcomes is often complicated by the absence of observable values in patients who die prior to their scheduled measurement. In such cases, the longitudinal data are said to be “truncated by death” to emphasize that the longitudinal measurements are not simply missing, but are undefined after death. Recently, the truncation by death problem has been investigated using the framework of principal stratification to define the target estimand as the survivor average causal effect (SACE), which in the context of a two-group randomized clinical trial is the mean difference in the longitudinal outcome between the treatment and control groups for the principal stratum of always-survivors. The SACE is not identified without untestable assumptions. These assumptions have often been formulated in terms of a monotonicity constraint requiring that the treatment does not reduce survival in any patient, in conjunction with assumed values for mean differences in the longitudinal outcome between certain principal strata. In this paper, we introduce an alternative estimand, the balanced-SACE, which is defined as the average causal effect on the longitudinal outcome in a particular subset of the always-survivors that is balanced with respect to the potential survival times under the treatment and control. We propose a simple estimator of the balanced-SACE that compares the longitudinal outcomes between equivalent fractions of the longest surviving patients between the treatment and control groups and does not require a monotonicity assumption. We provide expressions for the large sample bias of the estimator, along with sensitivity analyses and strategies to minimize this bias. We consider statistical inference under a bootstrap resampling procedure.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:ijbist:v:9:y:2013:i:2:p:291-306:n:1
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    References listed on IDEAS

    as
    1. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. repec:mpr:mprres:5880 is not listed on IDEAS
    3. Schechtman, Edna & Shelef, Amit & Yitzhaki, Shlomo & Zitikis, RiÄ ardas, 2008. "Testing Hypotheses About Absolute Concentration Curves And Marginal Conditional Stochastic Dominance," Econometric Theory, Cambridge University Press, vol. 24(4), pages 1044-1062, August.
    4. 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.
    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. Haim Shalit & Shlomo Yitzhaki, 1994. "Marginal Conditional Stochastic Dominance," Management Science, INFORMS, vol. 40(5), pages 670-684, May.
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