"Clinical studies often encounter truncation by death, which may render some outcomes undefined. Statistical analysis based solely on observed survivors may lead to biased results because the characteristics of survivors may differ between treatment groups. In this case, the commonly used meaningful causal parameter is the survivor average causal effect (SACE), which may not be identifiable when there is unmeasured confounding between the treatment assignment and survival or outcome processes. In this talk, we first show that the survivor average causal effect on the control is identifiable based on a substitutional variable under appropriate assumptions.
Next, we propose an augmented inverse probability weighting (AIPW) type estimator for this estimand with robustness to model misspecification. Finally, the proposed method is applied to investigate the effects of allogeneic stem cell transplantation types on leukemia relapse. This is a joint work with Drs. Yuhao Deng and Y. Chang at Peking University."

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