HOD: Handling missing data and time-varying confounding in causal inference for observational event history data

在医学上，对一种新疗法的效果（包括有益的和有害的）进行有效的估计通常是很重要的。为了做到这一点，我们通常比较一组接受新治疗的患者（治疗组）和未接受新治疗的患者（对照组）的结果。随机对照试验（RCT）是获得这些治疗效果估计的黄金标准，因为它公平地将患者分配到两组，这使得它们在治疗开始之前可能具有可比性，例如，一组不会更老或更年轻，更病或更健康等等。然而，随机对照试验非常昂贵且运行复杂，并不一定适合回答有关治疗效果的所有问题。例如，一种药物的副作用可能导致癌症，但癌症可能只是在治疗几年后才出现。因此，一项随机对照试验不太可能维持足够长的时间来检测这种影响。因此，仅通过查看作为正常护理一部分接受治疗的患者的数据（通过“观察性研究”）来衡量治疗效果将非常有用。然而，与随机对照试验不同的是，在观察性研究中，研究人员无法控制患者接受不同治疗方案的分配，因此，给予不同药物的患者组也可能在其他方面存在差异。例如，病情更严重的患者可能更有可能被给予能够改善病情但有令人不快的副作用的药物。如果在两组之间发现了结果的差异，则尚不清楚这种差异是由于两组之间的差异而不仅仅是所接受的药物不同，还是这种差异确实是由治疗引起的（即它是一种“因果效应”）。在估计因果关系时，一种广泛使用的使群体更具可比性的方法是计算倾向得分。对于每个患者，他/她的倾向评分是根据患者在做出治疗决定时的特征预测接受特定治疗的概率。平均而言，具有相同倾向评分但不同治疗方法的患者组应具有所有特征的可比性，因此组间结果的任何差异应归因于治疗。该项目的目的是扩展获得因果治疗效果的标准方法，以便在缺少有关患者特征的重要信息以及患者的治疗随时间变化时可以使用这些方法。这两种情况在观察性研究中都很常见，因此有可靠而有力的方法来处理它们是很重要的。我们提出了一项方法学研究计划，以解决观察性研究中的上述情况，特别关注治疗对临床事件发生时间的影响（例如，患者在手术后存活多长时间，或者在开始新治疗后多久开始出现令人不快的副作用）。本项目将为在医学研究中使用观察数据的从业人员提供一个总体框架和指导方针。

In medicine it is often important to obtain valid estimates of the effects (both beneficial and detrimental) of a new treatment. To do this, we typically compare outcomes in a group of patients who received the new treatment (treatment group) with those who did not receive the new treatment (control group). The randomised controlled trial (RCT) is the gold standard for obtaining these estimates of treatment effects because it fairly allocates patients to the two groups, which makes them likely to be comparable prior to the start of treatment, e.g. one group will not be older or younger, sicker or healthier and so on. However, RCTs are very expensive and complicated to run, and are not necessarily appropriate for answering all questions about the effects of treatment. For example, a drug may cause cancer as a side-effect, but the cancers may only appear after several years of treatment. It is then unlikely that an RCT would be maintained for long enough to detect this effect. It would therefore be very useful to measure the effects of treatments by looking only at data about patients who received the treatments as part of their normal care (through "observational studies").However unlike in RCTs, the investigators have no control over the assignment of patients to different treatment regimens in observational studies and therefore groups of patients given different drugs may differ in other ways as well. For example, patients with more severe disease may be more likely to be given drugs which are good at improving the disease but have unpleasant side-effects. If there is a difference in outcome found between the groups, it is not clear whether the difference is due to the fact that the groups are different beyond just the drugs received, or whether the difference was really caused by the treatment (i.e. it was a "causal effect"). One widely used method to make groups more comparable when estimating the causal effect is to calculate propensity scores. For each patient, his/her propensity score is the predicted probability of receiving a particular treatment based on that patient's characteristics at the time the treatment decision is made. Groups of patients with the same propensity score but different treatments should, on average, be comparable for all of their characteristics, and any differences in outcome between the groups should therefore be attributable to treatment.The aim of this project is to extend standard methods for obtaining causal treatment effects so that they can be used when important information about patient characteristics is missing and when patient's treatment changes over time. Both of these situations are common in observational studies, thus it is important to have reliable and robust ways to deal with them. We propose a programme of methodological research to address the above situations in observational studies, with a particular focus on the effect of treatments on the time to clinical events (e.g. how long does a patient survive after a surgery, or how soon after the start of a new treatment do unpleasant side-effects start appearing). This project will provide a general framework and guidelines for practitioners who use observational data in medical research.