Rejoinder to Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with "Censoring" Due to Death

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with "Censoring" Due to Death

Causal inference is best understood using potential outcomes. This use is particularly important in more complex settings, that is, observational studies or randomized experiments with complications such as noncompliance. The topic of this lecture, the issue of estimating the causal effect of a treatment on a primary outcome that is ``censored'' by death, is another such complication. For example, suppose that we wish to estimate the effect of a new drug on Quality of Life (QOL) in a randomized experiment, where some of the patients die before the time designated for their QOL to be assessed. …

Estimating the effect of PEG in ALS patients using observational data subject to censoring by death and missing outcomes

Though they may offer valuable patient and disease information that is impossible to study in a randomized trial, clinical disease registries also require special care and attention in causal inference. Registry data may be incomplete, inconsistent, and subject to confounding. In this paper we aim to address several analytical issues in estimating treatment effects that plague clinical registries such as the Emory amyotrophic lateral sclerosis (ALS) Clinic Registry. When attempting to assess the effect of a surgical insertion of a percutaneous endoscopic gastrostomy (PEG) tube on body mass ind…

An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs

Participants in longitudinal studies on the effects of drug treatment and criminal justice system interventions are at high risk for institutionalization (e.g., spending time in an environment where their freedom to use drugs, commit crimes, or engage in risky behavior may be circumscribed). Methods used for estimating treatment effects in the presence of institutionalization during follow-up can be highly sensitive to assumptions that are unlikely to be met in applications and thus likely to yield misleading inferences. In this paper we consider the use of principal stratification to control …

通过主分层评估存在治疗转换时的因果效应

Assessing causal effects in the presence of treatment switching through principal stratification

该研究通过主分层重新定义治疗转换问题，采用贝叶斯方法处理连续时间转换与删失，分析Concorde合成数据，发现立即使用齐多夫定增加从未转换者生存时间且效果异质性高。

比例主层风险模型研究

On the Proportional Principal Stratum Hazards Model

针对临床试验中死亡竞争风险下无法准确估计治疗对非致死事件直接效应的问题，提出比例主层风险模型，通过共享脆弱模型识别主层归属，经模拟验证可靠性并在心力衰竭临床试验中完成演示。