To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, w...To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, we demonstrate that the partial least squares method could be a valuable tool in this regard. It is showed that the partial least squares method not only can adequately account for the heterogeneity in treatment assignment, but also be robust to treatment assignment model misspecifications. Numerical results show that the partial least squares estimator is more efficient and robust. A real data set is analyzed to illustrate the proposed method.展开更多
基金Supported by the National Natural Science Foundation of China(11501578,11701571)
文摘To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, we demonstrate that the partial least squares method could be a valuable tool in this regard. It is showed that the partial least squares method not only can adequately account for the heterogeneity in treatment assignment, but also be robust to treatment assignment model misspecifications. Numerical results show that the partial least squares estimator is more efficient and robust. A real data set is analyzed to illustrate the proposed method.
