Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis.
Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis.
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DOI：
10.1186/s12874-016-0272-6
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发表时间：
2016-12-05
影响因子：
4
通讯作者：
Tudur-Smith C
中科院分区：
文献类型：
--
作者：
Sudell M;Kolamunnage-Dona R;Tudur-Smith C
纵向和事件发生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。使用荟萃分析从多项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关节模型的报告标准。在这次审查中，我们的目标是评估目前的标准，报告的联合模型中应用的文献，并确定目前的报告标准是否会允许或阻碍未来的汇总数据荟萃分析模型的结果。我们对涉及纵向和事件发生时间医学数据联合建模的非方法学研究进行了文献综述。提取研究特征，并评估是否可能对纵向、至事件发生时间和相关性参数进行单独的荟萃分析。所确定的65项研究在一系列软件中使用了广泛的联合建模方法。确定的研究涉及各种疾病领域。大多数研究报告了进行荟萃分析的充分信息（纵向参数汇总数据荟萃分析为67.7%，至事件时间参数汇总数据荟萃分析为69.2%，关联参数汇总数据荟萃分析为76.9%）。在某些情况下，很难从已发表的报告中确定模型结构。虽然在大多数情况下可以提取足够的信息进行荟萃分析，但应保持和改进联合模型的报告标准。对未来做法的建议包括明确说明模型结构、估计参数值、所用软件和所用统计方法。本文的在线版本（doi：10.1186/s12874-016-0272-6）包含补充材料，可供授权用户使用。
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. The online version of this article (doi:10.1186/s12874-016-0272-6) contains supplementary material, which is available to authorized users.
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影响因子：
4
作者：
Deslandes E;Chevret S
通讯作者：
Chevret S
影响因子：
2
作者：
Andrinopoulou, Eleni-Rosalina;Rizopoulos, Dimitris;Lesaffre, Emmanuel
通讯作者：
Lesaffre, Emmanuel
DOI：
10.1007/s10742-011-0074-6
发表时间：
2011-12-01
影响因子：
1.5
作者：
Du, Hongyan;Hahn, Elizabeth A.;Cella, David
通讯作者：
Cella, David
影响因子：
3.5
作者：
Fairclough, Diane L.;Thijs, Herbert;Wu, Albert W.
通讯作者：
Wu, Albert W.
影响因子：
3.7
作者：
Argyropoulos C;Roumelioti ME;Sattar A;Kellum JA;Weissfeld L;Unruh ML
通讯作者：
Unruh ML