Meta‐analysis of clinical trials with competing time‐to‐event endpoints. Issue 3 (9th December 2019)

• Record Type: Journal Article
• Authors: Meddis, Alessandra; Latouche, Aurélien; Zhou, Bingqing; Michiels, Stefan; Fine, Jason
• Is Part Of: Biometrical journal. Volume 62:Issue 3(2020:May)
• Page Start: 712
• Page End: 723
• Publication Date: 2019-12-09
• Subjects: cause‐specific hazards -- competing endpoints -- cumulative incidence function -- heterogeneity -- IPD meta‐analysis; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900103
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: Recommendations for the analysis of competing risks in the context of randomized clinical trials are well established. Meta‐analysis of individual patient data (IPD) is the gold standard for synthesizing evidence for clinical interpretation based on multiple studies. Surprisingly, no formal guidelines have been yet proposed to conduct an IPD meta‐analysis with competing risk endpoints. To fill this gap, this work details (i) how to handle the heterogeneity between trials via a stratified regression model for competing risks and (ii) that the usual metrics of inconsistency to assess heterogeneity can readily be employed. Our proposal is illustrated by the re‐analysis of a recently published meta‐analysis in nasopharyngeal carcinoma, aiming at quantifying the benefit of the addition of chemotherapy to radiotherapy on each competing endpoint.