Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this a...Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this article,we estimate the survival treatment effect with covariate adjustment using propensity score in the semiparametric accelerated failure time model.We establish the asymptotic properties of the proposed estimator by simultaneous estimating equations.We conduct simulation studies to evaluate the finite sample performance of the proposed method.A real data set from the German Breast Cancer Study Group is analyzed to illustrate the proposed method.展开更多
To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g com...To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g computation as a reliable method of covariate adjustment.How-ever,the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest.To fill this gap,we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.展开更多
When an independent estimate of covariance matrix is available, we often prefer two-stage estimate （TSE）. Expressions of exact covarianee matrix of the TSE obtained by using all and some covariables in eovariance ad...When an independent estimate of covariance matrix is available, we often prefer two-stage estimate （TSE）. Expressions of exact covarianee matrix of the TSE obtained by using all and some covariables in eovariance adjustment approach are given, and a necessary and sufficient condition for the TSE to be superior to the least square estimate and related large sample test is also established. Furthermore the TSE, by using some covariables, is expressed as weighted least square estimate. Basing on this fact, a necessary and sufficient condition for the TSE by using some covariables to be superior to the TSE by using all eovariables is obtained. These results give us some insight into the selection of covariables in the TSE and its application.展开更多
基金the National Natural Science Foundation of China(Grant Nos.11501578 and 11701571)the Fundamental Research Funds for the Central Universities(Grant No.31512111206)。
文摘Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this article,we estimate the survival treatment effect with covariate adjustment using propensity score in the semiparametric accelerated failure time model.We establish the asymptotic properties of the proposed estimator by simultaneous estimating equations.We conduct simulation studies to evaluate the finite sample performance of the proposed method.A real data set from the German Breast Cancer Study Group is analyzed to illustrate the proposed method.
基金This work was supported by National Institute of Allergy and Infectious Diseases[NIAID 5 UM1 AI068617].
文摘To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g computation as a reliable method of covariate adjustment.How-ever,the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest.To fill this gap,we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.
基金The work is supported by the National Natural Science Foundation of China （10271010）, the Natural Science Foundation of Beijing （1032001）
文摘When an independent estimate of covariance matrix is available, we often prefer two-stage estimate （TSE）. Expressions of exact covarianee matrix of the TSE obtained by using all and some covariables in eovariance adjustment approach are given, and a necessary and sufficient condition for the TSE to be superior to the least square estimate and related large sample test is also established. Furthermore the TSE, by using some covariables, is expressed as weighted least square estimate. Basing on this fact, a necessary and sufficient condition for the TSE by using some covariables to be superior to the TSE by using all eovariables is obtained. These results give us some insight into the selection of covariables in the TSE and its application.
