CAUSAL INFERENCE AND LONGITUDINAL AIDS STUDIES

描述（改编自申请人的摘要）拟议的研究将 发展纵向因果推断的统计半参数方法 具有时间依赖性治疗策略的研究，时间依赖性混杂因素 和协变量，对关注的结局和协变量进行信息删失 和信息监测方案。开发此类方法的模板 反事实因果推理模型的统一， 删失数据模型反事实因果推理模型将被用作 实际的数据模型，其中处理机制被概括为 “行动机制”，代表监测、审查和治疗行动。 通过将该模型视为删失数据模型，可以利用 删失数据模型的估计理论。这提供了 局部有效估计量的构建蓝图， 一致性只依赖于正确的动作模型规范 方法（Gill，货车der Laan，Robins）。这些通用方法在 “研究”这一建议。 拟议的项目是（a）为最常见的 因果推理和审查数据模型;（B）开发计算机程序， 模拟复杂纵向数据和纯删失数据结构， 使用所提出的估计量来估计感兴趣的因果参数; (c)将这些方法应用于艾滋病研究的各种数据集。 拟议中的研究将允许从以下方面得出因果推论： 观察数据，即使存在复杂因素， 信息处理分配和信息删失。的工具 分析这些数据在艾滋病研究等领域将是非常宝贵的， 行动策略必须根据患者的病史进行调整， 对混杂变量（如病毒载量）变化的反应。

DESCRIPTION (Adapted from applicant's abstract) The proposed research will develop statistical semiparametric methods for causal inference in longitudinal studies with time-dependent treatment strategies, time-dependent confounders and covariates, informative censoring on the outcome of interest and covariates and informative monitoring schemes. A template for developing such methods results from the unification of counterfactual causal inference models and censored data models. The counterfactual causal inference model will be used as the actual data model, where the treatment mechanism is generalized to an "action mechanism," representing monitoring, censoring, and treatment action. By also viewing this model as a censored data model, it is possible to exploit the estimation theory developed for censored data models. This provides a blueprint for the construction of locally efficient estimators whose consistency relies only on correct specification of models for the action process (Gill, van der Laan, Robins). This general methods is described in the "research section" of this proposal. The proposed project is to (a) develop the estimators for the most common causal inference and censored data models; (b) develop computer programs to simulate both complex longitudinal data and pure censored data structures and to estimate the causal parameters of interest using the proposed estimators; (c) to apply the methods to various data sets from AIDS studies. The proposed research will allow causal inferences to be drawn from observational data, even in the presence of complicating factors such as informative treatment assignment and informative censoring. The tools to analyze such data will be invaluable in areas such as AIDS research, where action strategies must be tailored to a patient's history and modified in response to changes in confounding variables such as viral load.