Departments of Medicine, of Health Research and Policy, of Biomedical Data Science and of Statistics, Stanford University, Stanford, CA, USA.
Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.
Int J Epidemiol. 2019 Apr 1;48(2):596-608. doi: 10.1093/ije/dyy239.
One of the claimed main advantages of individual participant data meta-analysis (IPDMA) is that it allows assessment of subgroup effects based on individual-level participant characteristics, and eventually stratified medicine. In this study, we evaluated the conduct and results of subgroup analyses in IPDMA.
We searched PubMed, EMBASE and the Cochrane Library from inception to 31 December 2014. We included papers if they described an IPDMA based on randomized clinical trials that investigated a therapeutic intervention on human subjects and in which the meta-analysis was preceded by a systematic literature search. We extracted data items related to subgroup analysis and subgroup differences (subgroup-treatment interaction p < 0.05).
Overall, 327 IPDMAs were eligible. A statistically significant subgroup-treatment interaction for the primary outcome was reported in 102 (36.6%) of 279 IPDMAs that reported at least one subgroup analysis. This corresponded to 187 different statistically significant subgroup-treatment interactions: 124 for an individual-level subgrouping variable (in 76 IPDMAs) and 63 for a group-level subgrouping variable (in 36 IPDMAs). Of the 187, only 7 (3.7%; 6 individual and 1 group-level subgrouping variables) had a large difference between strata (standardized effect difference d ≥ 0.8). Among the 124 individual-level statistically significant subgroup differences, the IPDMA authors claimed that 42 (in 21 IPDMAs) should lead to treating the subgroups differently. None of these 42 had d ≥ 0.8.
Availability of individual-level data provides statistically significant interactions for relative treatment effects in about a third of IPDMAs. A modest number of these interactions may offer opportunities for stratified medicine decisions.
个体参与者数据荟萃分析（IPDMA）的一个声称的主要优势是，它允许根据个体参与者特征评估亚组效应，并最终实现分层医学。在这项研究中，我们评估了 IPDMA 中亚组分析的实施和结果。
我们从 1966 年 1 月 1 日至 2014 年 12 月 31 日在 PubMed、EMBASE 和 Cochrane Library 进行了检索。如果文献描述了基于随机对照试验的 IPDMA，该试验调查了人类受试者的治疗干预措施，并且荟萃分析之前进行了系统的文献检索，则纳入研究。我们提取了与亚组分析和亚组差异（亚组-治疗相互作用 p<0.05）相关的数据项。
共有 327 项 IPDMA 符合纳入标准。在报告了至少一次亚组分析的 279 项 IPDMA 中，有 102 项（36.6%）报告了主要结局的统计学显著亚组-治疗相互作用。这相当于 187 个不同的统计学显著亚组-治疗相互作用：124 个为个体水平的亚组变量（在 76 项 IPDMA 中），63 个为群体水平的亚组变量（在 36 项 IPDMA 中）。在这 187 个中，只有 7 个（3.7%；6 个个体和 1 个群体亚组变量）在分层之间有较大差异（标准化效应差异 d≥0.8）。在 124 个个体水平的统计学显著亚组差异中，IPDMA 作者声称 42 个（在 21 项 IPDMA 中）应该导致对亚组进行不同的治疗。这些亚组中没有一个 d≥0.8。
个体水平数据的可用性为 IPDMA 中约三分之一的相对治疗效果提供了统计学显著的相互作用。这些相互作用中有相当数量可能为分层医学决策提供机会。
