Analysis of trials, meta-analyses and observational studies

我们涵盖的问题，如生存分析方法，允许在试验和IPD荟萃分析，预后和预测因素的建模，以及纵向和聚类数据的分析中的非比例风险。在因果模型的应用方面，CTU试验是评估患者管理方面的丰富资源，而不是随机比较，例如二线或合并治疗的影响。虽然通常由临床医生自行决定，但试验通常会根据这些决定收集信息。利用这些数据在艾滋病毒DART试验的主要因果分析中，我们发现，24周和12周的CD 4监测得到了类似的结果，并且在48周时单次CD 4计数比没有CD 4监测更好的生存率。最后，关于缺失数据，即使是少量的缺失数据也会导致偏倚，使研究结论不可靠和/或不精确。一些处理它的方法可能会导致进一步的偏差或不精确，但在许多疾病领域普遍存在，并被一些监管机构推荐。我们与伦敦卫生和热带医学学院（LSHTM）合作，提出了一个新的框架，用于分析缺失数据的临床试验，该框架已被药物信息协会工作组和制药公司监管申报采用。

We cover issues such as survival analysis methods that allow for non-proportional hazards in both trials and IPD meta-analyses, modelling of prognostic and predictive factors, and analysis of longitudinal and clustered data. On the application of causal models, CTU trials are a rich resource for evaluating aspects of patient management other than the randomised comparison, such as the impact of second-line or concomitant treatments. Although often left to clinician discretion, the trial will usually collect information on the basis for these decisions. Exploiting such data in a major causal analysis of the DART trial in HIV, we showed that 24-weekly and 12 weekly CD4 monitoring give similar results, and that a single CD4 count at 48 weeks leads to better survival than no CD4 monitoring. Finally, on missing data, even modest amounts of missing data can lead to bias and make study conclusions unreliable and/or imprecise. Some methods to deal with it can lead to further bias or imprecision, yet prevail in many disease areas, and are recommended by some regulators. In collaboration with the London School of Hygiene and Tropical Medicine (LSHTM), we have proposed a new framework for the analysis of clinical trials with missing data, which has been adopted by a Drug Information Association working group and for a pharmaceutical company regulatory submission.