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

HOD：处理观测事件历史数据因果推断中的缺失数据和时变混杂

• 批准号: MR/M025152/2
• 负责人: Jian Shi
• 金额: $ 29.21万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM025152%2F2
• 关键词: HOD; Handling; missing; data; time

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.

在医学中，获得对新治疗效果（有益和有害）的有效估计通常很重要。为此，我们通常比较接受新治疗的患者（治疗组）与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值的金标准，因为它将患者公平地分配到两组，这使得它们在治疗开始前可能具有可比性，例如，一组不会更老或更年轻，病情更重或更健康等。然而，RCT非常昂贵且运行复杂，并且不一定适合回答关于治疗效果的所有问题。例如，一种药物可能会导致癌症的副作用，但癌症可能只会在治疗几年后出现。因此，RCT不太可能维持足够长的时间来检测这种效应。因此，仅通过观察接受常规治疗的患者的数据（通过“观察性研究”）来衡量治疗效果是非常有用的。然而，与随机对照试验不同，观察性研究中研究者无法控制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存在差异。例如，病情较严重的病人可能更有可能服用有助于改善病情但有不良副作用的药物。如果两组之间的结果存在差异，尚不清楚这种差异是否是由于两组之间的差异而不仅仅是所接受的药物，或者这种差异是否真的是由治疗引起的（即，这是一种“因果效应”）。在估计因果效应时，一种广泛使用的使群体更具可比性的方法是计算倾向分数。对于每名患者，他/她的倾向评分是基于做出治疗决定时患者的特征而预测的接受特定治疗的概率。平均而言，具有相同倾向评分但治疗不同的患者组的所有特征都应具有可比性，因此，两组之间的任何结果差异都应归因于治疗。本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和患者治疗改变时使用随着时间这两种情况在观察性研究中都很常见，因此必须有可靠和稳健的方法来处理它们。我们提出了一个方法学研究计划，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，手术后患者存活多久，或开始新治疗后多久开始出现不愉快的副作用）。该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南。

项目成果

Conditional quantile correlation learning for ultrahigh dimensional varying coefficient models and its application in survival analysis

• DOI: 10.5705/ss.202016.0402
• 发表时间: 2018
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Xiaochao Xia;Jialiang Li;B. Fu

Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions

非对称重尾分布下的异方差重复测量误差模型

• DOI: 10.1007/s00180-017-0720-8
• 发表时间: 2018-03
• 期刊: Computational Statistics, Online, DOI: 10.1007/s00180-017-0720-8
• 作者: Cao Chunzheng;Chen Mengqian;Wang Yahui;Shi Jian Qing
• 通讯作者: Shi Jian Qing