TY - JOUR
T1 - How often can meta-analyses of individual-level data individualize treatment?
T2 - A meta-epidemiologic study
AU - Schuit, Ewoud
AU - Li, Alvin H
AU - Ioannidis, John P A
N1 - Funding Information:
This work was supported by the Netherlands Organisation for Scientific Research (project 825.14.001 to E.S.); the Laura and John Arnold Foundation (METRICS to J.P.A.I.); Sue and Bob O’Donnell (to J.P.A.I.). Canadian Institute for Health Research Doctoral Scholarship with a Michael Smith Foreign Study Supplement (to A.L.). The funding sources had no role in the study design, data collection, analysis, preparation of the manuscript or decision to submit the manuscript for publication.
Publisher Copyright:
© The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
PY - 2019/4
Y1 - 2019/4
N2 - 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 interaction p < 0.05).Results: 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.Conclusions: 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.
AB - 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 interaction p < 0.05).Results: 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.Conclusions: 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.
KW - Individual participant data meta-analysis
KW - Subgroup analysis
KW - Individual patient data meta-analysis
KW - IPDMA
KW - Aggregate data meta-analysis
KW - Differential treatment effect
KW - differential treatment effect
KW - individual patient data meta-analysis
KW - individual participant data meta-analysis
KW - subgroup analysis
KW - aggregate data meta-analysis
UR - http://www.scopus.com/inward/record.url?scp=85067548363&partnerID=8YFLogxK
U2 - 10.1093/ije/dyy239
DO - 10.1093/ije/dyy239
M3 - Article
C2 - 30445577
SN - 0300-5771
VL - 48
SP - 596
EP - 608
JO - International Journal of Epidemiology
JF - International Journal of Epidemiology
IS - 2
ER -