NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.
National Institute for Health and Care Excellence (NICE): NICE Decision Support Unit Technical Support Documents [Internet].
This paper sets out a generalised linear model (GLM) framework for the synthesis of data from randomised controlled trials (RCTs). We describe a common model taking the form of a linear regression for both fixed and random effects synthesis, that can be implemented with Normal, Binomial, Poisson, and Multinomial data. The familiar logistic model for meta- analysis with Binomial data is a GLM with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes, by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pair-wise meta-analysis, indirect comparisons, synthesis of multi-arm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction.
We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo (MCMC) simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different RCTs report outcomes in different formats but from a common underlying model. Use of the GLM framework allows us to present a unified account of how models can be compared using the Deviance Information Criterion (DIC), and how goodness of fit can be assessed using the residual deviance. WinBUGS code for model critique is provided. Our approach is illustrated through a range of worked examples for the commonly encountered evidence formats, including shared parameter models.
We give suggestions on computational issues that sometimes arise in MCMC evidence synthesis, and comment briefly on alternative software.
Preliminary version: HTML in process
- NLM CatalogRelated NLM Catalog Entries
- Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials.[Med Decis Making. 2013]Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials.Dias S, Sutton AJ, Ades AE, Welton NJ. Med Decis Making. 2013 Jul; 33(5):607-17. Epub 2012 Oct 26.
- Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions.[Biometrics. 2016]Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions.Roy V, Evangelou E, Zhu Z. Biometrics. 2016 Mar; 72(1):289-98. Epub 2015 Aug 31.
- Bayesian hierarchical models for multi-level repeated ordinal data using WinBUGS.[J Biopharm Stat. 2002]Bayesian hierarchical models for multi-level repeated ordinal data using WinBUGS.Qiu Z, Song PX, Tan M. J Biopharm Stat. 2002 May; 12(2):121-35.
- Review An Empirical Assessment of Bivariate Methods for Meta-Analysis of Test Accuracy[ 2012]Review An Empirical Assessment of Bivariate Methods for Meta-Analysis of Test AccuracyDahabreh IJ, Trikalinos TA, Lau J, Schmid C. 2012 Nov
- Review Applications of Monte Carlo Simulation in Modelling of Biochemical Processes.[Applications of Monte Carlo Me...]Review Applications of Monte Carlo Simulation in Modelling of Biochemical Processes.Tenekedjiev KI, Nikolova ND, Kolev K. Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science. 2011 Feb 28
- NICE DSU Technical Support Document 2: A Generalised Linear Modelling Framework ...NICE DSU Technical Support Document 2: A Generalised Linear Modelling Framework for Pairwise and Network Meta-Analysis of Randomised Controlled Trials
Your browsing activity is empty.
Activity recording is turned off.
See more...