Analysis of trials, meta-analyses and observational studies

我们涵盖了一些问题，例如在试验和IPD荟萃分析中允许非比例风险的生存分析方法，预后和预测因素的建模，以及纵向和聚集数据的分析。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，例如二线或伴随治疗的影响。尽管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。在艾滋病毒DART试验的主要因果分析中利用这些数据，我们表明，24周和12周的CD4监测给出了类似的结果，48周时单一的CD4计数导致了比没有CD4监测的更好的存活率。最后，对于丢失的数据，即使是少量的丢失数据也可能导致偏差，并使研究结论不可靠和/或不准确。一些应对方法可能会导致进一步的偏差或不准确，但在许多疾病领域仍然流行，并被一些监管机构推荐。与伦敦卫生和热带医学院(LSHTM)合作，我们提出了一个新的框架，用于分析有缺失数据的临床试验，该框架已被药物信息协会工作组和一家制药公司的监管提交采用。

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.