Bayesian Network Meta-Analysis With One or Two Continuous Outcomes Measured at Multiple Time Points Using Gaussian Random Walks With Drift.

Network meta-analysis of randomized controlled trials is traditionally conducted on a single outcome measured at one time point. However, many trials also feature a secondary outcome and both outcomes may have been reported at multiple time points. Existing network meta-analysis methods for synthesizing continuous outcome data from such trials focus on either the longitudinal data aspect or the multiple outcomes aspect, but not on both simultaneously. In this paper, we present two Bayesian network meta-analysis models that account for the correlation of outcome measurements over time using Gaussian random walks with drift. The first model is suitable for a single continuous outcome measured at multiple time points, while the second model extends the first model to allow incorporation of a second outcome through cointegration of random walks. A simulation study to evaluate several statistical properties of these models is conducted. The results indicate that both proposed models produce unbiased estimates of relative treatment effect and drift parameters, as well as reasonable coverage. Furthermore, in some scenarios, using the cointegration model yields small gains in precision over using the single outcome model. Based on various performance measures, both proposed models also outperform an existing random walk network meta-analysis model previously used by investigators to synthesize osteoarthritis trials data. The proposed models are illustrated with an application to trials evaluating treatments for knee and hip osteoarthritis. Both models are useful additions to existing tools available to investigators undertaking a network meta-analysis of continuous outcome data at multiple time points.