Data synthesis over a network of multiple treatment comparisons for joint longitudinal and event-time outcomes.

通过多个治疗比较网络进行数据合成，以获取联合纵向和事件时间结果。

• 批准号: MR/S019251/1
• 负责人: Maria Sudell
• 金额: $ 30.2万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS019251%2F1
• 关键词: Data; synthesis; over; network; multiple

When examining data concerning a particular disease, it is beneficial to examine relevant data from multiple data sources, for example the results of multiple studies, datasets from several different hospitals or centres etc. The populations may differ between these different data sources, resulting in variability in response between data from different studies, hospitals or centres. This needs to be accounted for in the analysis. Meta-analysis is an approach to analyse data from multiple different sources, whilst accounting for differences between the data sources.Commonly a range of different information is collected from individuals concerning their health. These could include items repeatedly measured over time, such as the results from weekly blood tests. They could also include the time until a specific event of interest occurs, such as the time until a disease progresses to the next stage. Joint modelling has been growing in popularity as a method to simultaneously model outcomes repeatedly measured over time, along with time until event outcomes. Recently, work has been conducted to expand joint modelling to the case of multiple data sources. For a given condition or disease, commonly there are more than two possible treatment options. In reality, it is likely that different sets of possible treatment options are examined across the different data sources. For example one study may have examined treatment options A, B and C, whilst another examined B, C, and D, and a third examined only A and C. Current multi-data source joint modelling methods require treatment options to be directly compared in all data sources, meaning that the data sources contributing to the analysis would have to compare the same set of treatment options. However, network meta-analysis methods allow analysis of data where the set of investigated treatment options is not identical between data sources, as it allows both direct information (e.g. A compared to C) and indirect information (A compared to B, B compared to C) to inform about the treatment comparison A versus C. As such, provided paths exist between the possible treatment options, network meta-analyses do not require the data sources to examine the same sets of treatment options.Network meta-analyses have not been extended into joint modelling methodology. This fellowship will develop this methodology, and implement it in free, easy to use statistical software. However, a known problem in joint modelling analyses is that joint models can be time intensive to fit, an issue that would be increasingly noticeable with large multi-data source analyses. During this fellowship, computer science and machine learning methods such as Sequential Monte Carlo or SMC samplers will be employed in an attempt to significantly reduce model fitting times for joint models. The developed methodology and software will be applied to three analyses; to compare treatment options for patients with HIV in an analysis of multi-hospital data containing time until relapse and markers such as CD4 cell count, to compare treatment options for hypertensive patients in a multi-study dataset containing systolic blood pressure and time to death measurements, and to compare treatment options for cardiovascular drug support for patients in different intensive care units in an analysis of time to treatment withdrawal along with various biomarkers.

在检查与特定疾病有关的数据时，有益的是检查来自多个数据源的相关数据，例如多项研究的结果、来自几个不同医院或中心的数据集等。这些不同的数据源之间的人群可能不同，导致来自不同研究、医院或中心的数据之间的反应不同。这一点需要在分析中加以考虑。Meta分析是一种分析来自多个不同来源的数据的方法，同时考虑到不同数据源之间的差异。通常只从个人收集关于他们健康的不同信息。这些可能包括随着时间的推移重复测量的项目，例如每周血液测试的结果。它们还可以包括特定感兴趣的事件发生之前的时间，例如疾病发展到下一阶段的时间。联合建模作为一种同时模拟随时间重复测量的结果以及直到事件结果的时间的方法越来越受欢迎。最近，已经开展工作，将联合建模扩展到多个数据源的情况。对于一种特定的情况或疾病，通常有两种以上的可能治疗方案。实际上，很可能会跨不同的数据源检查不同的可能治疗选项集。例如，一项研究可能检查了处理选项A、B和C，而另一项研究检查了B、C和D，而第三项研究仅检查了A和C。当前的多数据源联合建模方法要求在所有数据源中直接比较处理选项，这意味着对分析作出贡献的数据源将必须比较同一组处理选项。然而，网络荟萃分析方法允许在所调查的治疗选项集合在数据源之间不相同的情况下分析数据，因为它允许直接信息(例如，A与C相比)和间接信息(A与B相比，B与C相比)两者来告知治疗比较A与C。因此，如果在可能的治疗选项之间存在路径，则网络荟萃分析不要求数据源检查相同的治疗选项集合。该研究金将开发这一方法，并在免费、易于使用的统计软件中实施。然而，联合建模分析中的一个已知问题是，联合模型的拟合可能需要很长时间，这一问题在大型多数据源分析中会越来越明显。在此期间，将使用计算机科学和机器学习方法，如顺序蒙特卡洛采样器或SMC采样器，试图显著减少联合模型的模型拟合时间。开发的方法和软件将应用于三项分析；在多家医院的数据分析中比较艾滋病毒患者的治疗方案，这些数据包括直到复发的时间和CD4细胞计数等指标，在包含收缩压和死亡时间测量的多研究数据集中比较高血压患者的治疗方案，以及在分析停药时间和各种生物标志物的情况下比较不同重症监护病房患者的心血管药物支持的治疗方案。

项目成果

Protocol for individual participant data meta-analysis of randomised controlled trials of patients with psychosis to investigate treatment effect modifiers for CBT versus treatment as usual or other psychosocial interventions.

• DOI: 10.1136/bmjopen-2019-035062
• 发表时间: 2021-05-28
• 期刊: BMJ open
• 影响因子: 2.9
• 作者: Sudell M;Tudur-Smith C;Liao X;Longden E;Dunn G;Kendall T;Emsley R;Morrison A;Varese F
• 通讯作者: Varese F