Methods for causal analysis of longitudinal data

医学和健康研究中最基本的问题之一是：“使用某种特定的治疗方法是否会使病人活得更长？“在某些情况下，这个问题可以通过使用随机试验来解决，但这种试验非常昂贵，可能需要多年才能完成。另一种方法是尝试使用所谓的观测数据来回答这个问题。即，已完成研究、登记研究或数据库中的可用数据，其中记录了一组受试者的治疗结局和一组患者特征。该研究计划的重点是治疗是动态的，并随着时间的推移而变化的设置。例如，在治疗过程中给予患者的药物剂量。我们假设受试者以规则间隔（天、周、月等）接受治疗。并且随着时间的推移，他们所接受的治疗的水平和/或类型是基于他们所观察到的特征的生长历史。治疗方案是描述如何确定下一个治疗水平的规则。此类设置的示例包括用于治疗透析患者贫血的依泊苷给药规则和用于心脏病患者的术后输血治疗。该研究项目的重点是开发统计模型和方法，以便根据观察数据对治疗方案进行有效比较。

One of the most fundamental questions in medical and health research is: "Does using a particular treatment make sick patients live longer?" In some settings this question can be addressed through the use of a randomized trial however such trials are very expensive and may take many years to complete. An alternative is to try and answer the question using what is known as observational data. That is, data available from completed studies, registries or databases in which the treatment outcome, and a set of patient characteristics have been recorded for a group of subjects. This research program is focused on settings in which treatment is dynamic and changing over time. For example, the dose of a drug that a patient is given over their course of treatment. We assume that subjects receive treatment at regularly spaced intervals (days, weeks, months, etc.) and that as time progresses the level and/or type of treatment they receive is based on their growing history of observed characteristics. A treatment regimen is a rule describing how their next treatment level is determined. Examples of such settings include rules for epoetin dosing to treat anemia in dialysis patients and post-surgical blood transfusion treatment for cardiac patients. This research program is focused on developing statistical models and methods that allow for valid comparison of treatment regimens based on observational data.