Causal Dynamic Predictive Models for Cross Sectional Observational Data

描述(由申请人提供)： 项目背景：监测退伍军人的医疗保健需要使用纵向数据。退伍军人医疗保健系统拥有极其丰富和高维的纵向数据，涉及大量年龄较小就成为使用者并与该系统共度余生的受试者。这些数据提供了关于各种疾病及其管理策略的一些独特的观察纵向队列。为了从这些观测的纵向数据中学习，需要适当的方法来减少维度和混杂的偏差。项目目标：建议的研究将开发一个模型，用于估计当我们面对动态情况时分组变量(干预)的效果。在这种设置下，我们的目标是：1.如何降低这些重复测量协变量的高维，使关于结果和分组变量(干预)的信息不丢失？2.如何从这些协变量的混杂效应中提取和估计干预的时间依赖效应？项目方法。为了达到降维和协变量平衡，我们构造了充分的摘要。一个充分的总结是协变量的参数函数，在给定其任何值的情况下，协变量分布不受干预水平的影响。为了找到一个充分的总结，我们应该知道的密度比 给予不同干预水平的协变量。当我们反复测量时，这些密度比很难现实地确定。若要简化模型而不使用 失去了对现实的描述，我们做了两个假设：1.响应、干预和每次访问(时间点)的协变量值取决于它们对应的最近过去值(这称为一阶马尔可夫性质)。2.在一次访问中，这三个变量中的每一个变量的值可能只取决于该访问中其他变量的状态。这对假设具有隐马尔可夫链结构。然后，对于一些非常重要的情况，我们得到了充分的总结。接下来，我们定义干预的因果效应。这是在协变量独立于干预的情况下干预的效果。我们证明了这种效应只需要充分的总结就可以计算出来。最后，由于这种影响依赖于模型的参数，为了估计所有时间点的这种影响，我们假设参数作为时间的函数具有一些动态的贝叶斯-马尔可夫结构。我们将应用约束最大后验方法来递归地估计参数，从而估计效果函数。对退伍军人的重要性：在开发在流行病学、临床和卫生服务研究中导致改进、更可靠的推断的方法时，拟议的研究将导致建立更健全的医疗干预和健康计划，这些将直接影响退伍军人的健康。在众多这类研究的过程中，这项研究的累积影响可能是巨大的。

DESCRIPTION (provided by applicant): Abstract Project Background: Monitoring veterans' health care requires use of longitudinal data. The VA health care system has an extremely rich and high-dimensional longitudinal data on a large number of subjects who become a user at an earlier age and stay with system for the rest of their life. This data provides some unique observational longitudinal cohorts on variety of diseases and their management strategies. For learning from these observational longitudinal data appropriate methodologies for dimension and confounding bias reduction are required. Project Objectives: The proposed research will develop a model for estimating the effect of a grouping variable (intervention) when we are faced with a dynamic situation. Under this setup, our objectives then are: 1. How to reduce the high dimension of these repeated measure covariates, so that no information is lost with respect to the outcome and the grouping variable (intervention)? 2. How to extract and estimate the time-dependent effect of the intervention from the confounding effect of these covariates? Project Methods. To achieve dimension reduction and covariate balance, we construct sufficient summaries. A sufficient summary is a parametric function of the covariates that given any of its values, the covariate distribution is free from intervention level. In order to find a sufficient summary, we should know the density ratios of the covariates given different levels of the intervention. When we have repeated measures these density ratios are extremely difficult to be specified realistically. To simplify the model without losing its description of reality, we make two assumptions: 1. Response, the intervention and the covariate values at each visit (time point) depend on their corresponding the recent past values (this is called Markov property of order one). 2. The value of each of these three variables at a given visit may only depend on the status of the other variables at that visit. This pair of assumptions has a Hidden Markov Chain structure. Then, we derive sufficient summaries for some very important cases. Next, we define the causal effect of the intervention. This is the effect of the intervention had the covariates were independent of the intervention. We show that this effect can be calculated only using the sufficient summaries. Finally, since this effect depends on the parameters of the model, in order to estimate this effect at all time points, we assume the parameters as a function of time have some Dynamic Bayes Markovian structures. We will apply restricted maximum a posteriori method to estimate the parameters and, hence, the effect function, recursively. Importance to VA: In developing methods that lead to improved, more reliable inference in epidemiological, clinical, and health services research, the proposed study will lead to more soundly established medical interventions and health programs that will directly impact veteran's health. Over the course of numerous such research studies the cumulative impact of this research could be substantial.