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。为此，我们通常比较接受新治疗的一组患者(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评估的黄金标准，因为它公平地将患者分配到两组，使他们在治疗开始前可能具有可比性，例如，一组不会年龄更大或更年轻、疾病更多或更健康等等。然而，随机对照试验非常昂贵和复杂，不一定适合回答所有关于治疗效果的问题。例如，一种药物可能会作为副作用导致癌症，但癌症可能只有在几年的治疗后才会出现。因此，RCT不太可能维持足够长的时间来检测这种影响。因此，通过只观察将治疗作为其正常护理的一部分而接受治疗的患者的数据来衡量治疗效果将是非常有用的。但与随机对照试验不同的是，研究人员无法控制在观察研究中将患者分配到不同的治疗方案，因此给予不同药物的患者组可能在其他方面也有所不同。例如，病情越严重的患者可能更有可能被给予有效改善疾病但有令人不快的副作用的药物。如果两组之间的结果存在差异，目前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，还是这种差异是否真的是由治疗造成的(即，这是一种“因果关系”)。在估计因果效应时，一种广泛使用的方法是计算倾向分数，以使群体更具可比性。对于每个患者，他/她的倾向分数是根据患者在做出治疗决定时的特征预测的接受特定治疗的可能性。倾向评分相同但治疗方法不同的患者组的所有特征平均而言应具有可比性，因此组之间的任何结果差异应归因于治疗。该项目的目的是扩展获得因果治疗效果的标准方法，以便在有关患者特征的重要信息缺失时以及当患者的治疗随着时间的推移发生变化时使用这些方法。这两种情况在观察性研究中都很常见，因此有可靠和可靠的方法来处理它们是很重要的。我们提出了一个方法学研究计划来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，或者在新的治疗开始后多久开始出现令人不快的副作用)。该项目将为在医学研究中使用观测数据的从业者提供一个一般框架和指导方针。

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.