Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, Annex of Bldg. 3, 5th Floor, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Astellas Pharma Inc., Tokyo, 2-5-1 Nihonbashi-Honcho, Chuo-ku, Tokyo, 103-8411, Japan.
Int J Clin Oncol. 2018 Jun;23(3):403-409. doi: 10.1007/s10147-018-1237-z. Epub 2018 Jan 12.
Individual patient data (IPD) meta-analysis is considered to be a gold standard when the results of several randomized trials are combined. Recent initiatives on sharing IPD from clinical trials offer unprecedented opportunities for using such data in IPD meta-analyses.
First, we discuss the evidence generated and the benefits obtained by a long-established prospective IPD meta-analysis in early breast cancer. Next, we discuss a data-sharing system that has been adopted by several pharmaceutical sponsors. We review a number of retrospective IPD meta-analyses that have already been proposed using this data-sharing system. Finally, we discuss the role of data sharing in IPD meta-analysis in the future.
Treatment effects can be more reliably estimated in both types of IPD meta-analyses than with summary statistics extracted from published papers. Specifically, with rich covariate information available on each patient, prognostic and predictive factors can be identified or confirmed. Also, when several endpoints are available, surrogate endpoints can be assessed statistically.
Although there are difficulties in conducting, analyzing, and interpreting retrospective IPD meta-analysis utilizing the currently available data-sharing systems, data sharing will play an important role in IPD meta-analysis in the future.
当合并几项随机试验的结果时，个体患者数据（IPD）荟萃分析被认为是金标准。最近关于分享临床试验 IPD 的倡议为在 IPD 荟萃分析中使用此类数据提供了前所未有的机会。
首先，我们讨论了一项长期前瞻性 IPD 荟萃分析在早期乳腺癌中产生的证据和获得的益处。接下来，我们讨论了几个制药赞助商采用的数据共享系统。我们回顾了已经使用该数据共享系统提出的一些回顾性 IPD 荟萃分析。最后，我们讨论了未来数据共享在 IPD 荟萃分析中的作用。
在这两种类型的 IPD 荟萃分析中，治疗效果的估计比从已发表论文中提取的汇总统计数据更可靠。具体来说，通过对每个患者的丰富协变量信息，可识别或确认预后和预测因素。此外，当有多个终点时，可以对替代终点进行统计学评估。
尽管利用现有的数据共享系统进行回顾性 IPD 荟萃分析存在困难，但在未来，数据共享将在 IPD 荟萃分析中发挥重要作用。
