Miscellaneous How often can meta-analyses of individual-level data individualize treatment? A meta- epidemiologic study Ewoud Schuit , 1,2,3,4 * Alvin H Li 1,5 and John P A Ioannidis 1,2 1 Departments of Medicine, of Health Research and Policy, of Biomedical Data Science and of Statistics, Stanford University, Stanford, CA, USA, 2 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA, 3 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands, 4 Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands and 5 Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada *Corresponding author. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Huispost nr. STR 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands. E-mail: e.schuit@umcutrecht.nl Editorial decision 27 September 2018; Accepted 8 October 2018 Abstract Background: 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. Methods: 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 inter- action p < 0.05). Results: Overall, 327 IPDMAs were eligible. A statistically significant subgroup–treatment in- teraction 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. Conclusions: Availability of individual-level data provides statistically significant interac- tions for relative treatment effects in about a third of IPDMAs. A modest number of these interactions may offer opportunities for stratified medicine decisions. V C The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association 596 International Journal of Epidemiology, 2019, 596–608 doi: 10.1093/ije/dyy239 Advance Access Publication Date: 15 November 2018 Original article Downloaded from https://academic.oup.com/ije/article/48/2/596/5184552 by guest on 19 October 2023