Multiple sclerosis and fracture risk: traditional meta-analysis versus mega-analysis of individual patient data

Abstract

Introduction The aim of this systematic review was to evaluate the difference between a traditional meta-analysis and a mega-analysis of individual patient data when combining observational studies. Materials and methods We used data from two studies that evaluated the risk of fracture in patients with multiple sclerosis using the British General Practice Research Database and the Danish National Health Registries. The published results were pooled together in an inverse-variance fixed effect meta-analysis. Using patient level data, we made the study populations as comparable as possible regarding the index date, calendar time, selection of incident/prevalent patient and follow-up. The individual patient data of these populations were combined in a mega-analysis. Cox proportional hazards models were used to estimate hazard ratios of fracture, adjusted for shared confounders. Results A traditional meta-analysis of the original studies resulted in pooled adjusted hazard ratios of 1.13 [95%CI 1.03–1.23] for any fracture, hazard ratio 1.22 [95%CI 1.07–1.41] for osteoporotic fracture, and hazard ratio 2.47 [95%CI 1.72–3.53] for hip fracture. The mega-analysis of individual patient data showed an adjusted hazard ratio of 1.20 [95%CI 1.12−1.28] for any fracture, hazard ratio 1.36 [95%CI 1.24–1.50] for osteoporotic fracture, and hazard ratio 3.27 [95%CI 2.65–4.04] for hip fracture. The traditional meta-analysis of the original studies showed significant heterogeneity, which disappeared in a meta-analysis that pooled the two more comparable studies together. This meta-analysis yielded similar results as the mega-analysis with individual patient data. Conclusion A crucial step in performing a multi-country study is to reduce the level of heterogeneity between studies as much as possible before combining the data.