Methods for causal analysis of longitudinal data

医学和健康研究中最基本的问题之一是：“使用一种特殊的治疗方法能让病人活得更长吗？”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。另一种选择是尝试使用所谓的观测数据来回答这个问题。也就是说，从已完成的研究、登记或数据库中获得的数据，其中记录了一组受试者的治疗结果和一组患者特征。这个研究项目的重点是治疗是动态的和随时间变化的。例如，病人在治疗过程中服用的药物剂量。我们假设受试者每隔一段时间（天、周、月等）接受治疗，随着时间的推移，他们接受的治疗水平和/或类型是基于他们观察到的特征的不断增长的历史。治疗方案是描述如何确定下一个治疗水平的规则。此类设置的例子包括治疗透析患者贫血的促生成素剂量规则和心脏病患者手术后输血治疗规则。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的比较。

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.