Parameterization of treatment effects for meta-analysis in multi-state Markov models

• Published in: Statistics in medicine 2011-01, Vol.30 (2), p.140-151
• Main Authors: Price, Malcolm J., Welton, Nicky J., Ades, A. E.
• Format: Article
• Language: English
• Subjects: asthma; Asthma - therapy; Bayesian analysis; Bayesian inference; Clinical trials; Comparative analysis; Disease Progression; event history data; evidence synthesis; Evidence-Based Medicine - statistics & numerical data; Humans; Markov analysis; Markov Chains; Medical statistics; Meta-analysis; Meta-Analysis as Topic; mixed treatment comparison; Models, Statistical; multi-state model; Parameter estimation; Randomized Controlled Trials as Topic - statistics & numerical data; Side effects; Simulation; Treatment Outcome
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.4059

Standard approaches to analysis of randomized controlled trials (RCTs) using Markov models make it difficult to generalize treatment effects to new patient groups and synthesize evidence across trials. This paper demonstrates how pair‐wise and mixed treatment comparison meta‐analysis can be applied to event history data for disease progression reported by RCTs. The data, in the form of aggregated discrete time transitions, have a multi‐nomial likelihood. In order for evidence synthesis to be performed a structured approach to modelling the differences in the effects of the different treatments must be taken. A multi‐state continuous‐time Markov model similar to others used in published economic evaluations of asthma treatments is developed, with transition rates related to the likelihood via Kolmogorov's forward equations. The formulation in terms of rates allows a flexible characterization of summary treatment effects. These ideas are applied to an illustrative data set consisting of a set of five trials comparing eight different treatments for asthma. A range of models is developed in which the relative treatment effects act on forward, backward transitions, or both, and models are compared using the DIC. Bayesian inferential techniques are used and the parameters are estimated using MCMC simulation in WinBUGS. An intuitively appealing mechanism of action involving a single parameter acting on all backward transitions was identified for the relative effects of the treatments, which allowed the estimation of a pooled treatment effect, allowing us to rank the different treatment options within each connected evidence network to ascertain which were the most clinically effective. Copyright © 2010 John Wiley & Sons, Ltd.