纵向和事件时间数据的联合模型通常用于同时分析单个研究案例中的相关数据。使用荟萃分析从多个研究中综合证据是自然的下一步,但其可行性在很大程度上取决于医学文献中联合模型的报告标准。在这篇综述中,我们的目的是评估目前在文献中应用的联合模型的报告标准,并确定当前的报告标准是否会允许或阻碍模型结果的未来汇总数据元分析。
我们进行了非方法学研究的文献综述,这些研究涉及纵向和事件发生时间医学数据的联合建模。提取研究特征,并评估是否对纵向进行单独的荟萃分析,事件发生时间和关联参数是可能的。
确定的65项研究在软件选择中使用了广泛的关节建模方法。确定的研究涉及各种疾病领域。大多数研究报告了足够的信息来进行荟萃分析(纵向参数汇总数据荟萃分析为67.7%,69.2%用于时间到事件参数聚合数据荟萃分析,76.9%用于关联参数汇总数据元分析)。在某些情况下,模型结构很难从已发布的报告中确定。
虽然在大多数情况下可以提取足够的信息来进行荟萃分析,应保持和改进联合模型的报告标准。对未来实践的建议包括明确说明模型结构,估计参数的值,使用的软件和应用的统计方法。
Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical literature. During this
review we aim to assess the current standard of reporting of joint models applied in the literature, and to determine whether current reporting standards would allow or hinder future aggregate data meta-analyses of model results.
We undertook a literature
review of non-methodological studies that involved joint modelling of longitudinal and time-to-event medical data. Study characteristics were extracted and an assessment of whether separate meta-analyses for longitudinal, time-to-event and association parameters were possible was made.
The 65 studies identified used a wide range of joint modelling methods in a selection of software. Identified studies concerned a variety of disease areas. The majority of studies reported adequate information to conduct a meta-analysis (67.7% for longitudinal parameter aggregate data meta-analysis, 69.2% for time-to-event parameter aggregate data meta-analysis, 76.9% for association parameter aggregate data meta-analysis). In some cases model structure was difficult to ascertain from the published reports.
Whilst extraction of sufficient information to permit meta-analyses was possible in a majority of cases, the standard of reporting of joint models should be maintained and improved. Recommendations for future practice include clear statement of model structure, of values of estimated parameters, of software used and of statistical methods applied.