Evidence Synthesis for Decision Making 2

By S. Dias, A. Sutton, A. Ades, N. Welton. Medical decision making, 2013

1,122 citations recorded by the source corpus.

We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model 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 pairwise meta-analysis, indirect comparisons, synthesis of multiarm 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 simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c72c5a00952b53efdb446b2f8bc705bb45ec679e

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Citations in the corpus

GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “In general, there are three main approaches in the meta-regression of study-level covariates for NMA (Cooper et al., 2009; Dias et al., 2013b): using different and unrelated interaction terms (coefficients), using exchangeable interaction terms, and using a common interaction term.”
NICE DSU Technical Support Document 18: Methods for population-adjusted indirect comparisons in submissions to NICE (2016, neutral): “( ) ( ) ( ) ˆ ˆ ˆ BC P AC AC AB AB      , (2)”
Network Meta-Analysis Using R: A Review of Currently Available Automated Packages (2014, methodology): “It models the relative effects (e.g., log-odds ratio) fitting a generalized linear model (GLM) under the Bayesian framework by linking to JAGS, OpenBUGS or WinBUGS as first described by Lu and Ades [7], and extended by others [6, 12, 28, 31].”
Ranking treatments in frequentist network meta-analysis works without resampling methods (2015, neutral): “The dietary fat data are provided in the supplement of [20].”
Evidence Synthesis for Decision Making 4 (2013, methodology): “The choice of the most appropriate scale is essentially an empirical one, although there is seldom enough evidence to decide on the basis of goodness of fit.(8,10) The choice of method used to test for inconsistency should be guided by the evidence structure.”