Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment.
Availability of individual patient-level data (IPD) broadens the scope of network meta-analysis (NMA) and enables us to incorporate patient-level information. Although IPD is a potential gold mine in biomedical areas, methodological development has been slow owing to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast-based and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision making. Furthermore, we can find subpopulations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via an MCMC algorithm. We illustrate this approach using diabetes data that include continuous bivariate efficacy outcomes and three baseline covariates and show its practical implications. Finally, we close with a discussion of our results, a review of computational challenges, and a brief description of areas for future research.
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, U.S.A.
Hwanhee
H
Hong
Eli Lilly and Company, Indianapolis, IN, U.S.A.
Haoda
H
Fu
Eli Lilly and Company, Indianapolis, IN, U.S.A.
Karen L
KL
Price
Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55405, U.S.A.
Bradley P
BP
Carlin
D000465:Algorithms; D001499:Bayes Theorem; D015415:Biomarkers; D003920:Diabetes Mellitus / Q000628:therapy*; D006801:Humans; D008499:Medical Records; D015201:Meta-Analysis as Topic; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data
D015415:Biomarkers
Bayesian hierarchical model; Markov chain Monte Carlo (MCMC); individual-patient data (IPD); multiple-treatment comparison (MTC); subgroup analysis