title: Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions.
abstract: Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to 'empty' reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating example that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence.
authors.affiliation: Centre for Reviews and Dissemination, University of York, York, YO10 5DD, UK.
authors.forename: G B
authors.initials: GB
authors.lastname: Stewart
authors.affiliation: Department of Statistical Science, Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia.
authors.forename: K
authors.initials: K
authors.lastname: Mengersen
authors.affiliation: Centre for Reviews and Dissemination, University of York, York, YO10 5DD, UK.
authors.forename: N
authors.initials: N
authors.lastname: Meader
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine; D005240:Feasibility Studies; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods; D015995:Prevalence; D015203:Reproducibility of Results; D012196:Review Literature as Topic; D018570:Risk Assessment / Q000379:methods; D012559:Schizophrenia / Q000401:mortality*; D012680:Sensitivity and Specificity; D012907:Smoking / Q000401:mortality*; D016540:Smoking Cessation / Q000706:statistics & numerical data*; D000074606:Smoking Prevention; D016019:Survival Analysis; D016896:Treatment Outcome
keywords: decision models; directed graphs; evidence informed decision making; meta-analysis; narrative synthesis; systematic review