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.
Subject(s)
Bayes Theorem , Review Literature as Topic , Schizophrenia/mortality , Smoking Cessation/statistics & numerical data , Smoking Prevention , Smoking/mortality , Data Interpretation, Statistical , Evidence-Based Medicine , Feasibility Studies , Humans , Outcome Assessment, Health Care/methods , Prevalence , Reproducibility of Results , Risk Assessment/methods , Sensitivity and Specificity , Survival Analysis , Treatment OutcomeABSTRACT
This study was designed to determine the factors that affect nonmedical participants' judgments in constructing a ranked waiting list for kidney patients requiring dialysis. Participants (N=167) were given a questionnaire that provided minimal demographic data about 16 hypothetical patients. Participants were requested to rank patients in order of priority for treatment. Each participant's personal demographic details were also obtained. Patients differed on four dimensions: gender, income, alcohol consumption, and religious beliefs, yielding a 2x2x2x2 design. The participants favoured for treatment included females over males, "poor" over "rich," nondrinkers over drinkers, and Christians over atheists. Results are discussed in terms of establishing democratic criteria and informing medical personnel on explicit factors which may affect their decision making, thus guarding against biases in judgment.
