Balancing the Elicitation Burden and the Richness of Expert Input When Quantifying Discrete Bayesian Networks.

Structured expert judgment (SEJ) is a method for obtaining estimates of uncertain quantities from groups of experts in a structured way designed to minimize the pervasive cognitive frailties of unstructured approaches. When the number of quantities required is large, the burden on the groups of experts is heavy, and resource constraints may mean that eliciting all the quantities of interest is impossible. Partial elicitations can be complemented with imputation methods for the remaining, unelicited quantities.

A structured expert judgement elicitation approach: how can it inform sound intervention decision-making to support household food security?

Objective: To examine structured expert judgement (SEJ) elicitation as a method to provide robust, defensible data for three determinants of household food security (food cost, household disposable income and physical access) for quantifying a proof-of-concept integrating decision support system for food security.

Design: SEJ elicitation is a validated method for obtaining unavailable data, but its use in household food security in high-income countries is novel. Investigate Discuss Estimate Aggregate (IDEA) elicitation protocol was implemented, including quantitative and qualitative elements.

Anticipated impacts of Brexit scenarios on UK food prices and implications for policies on poverty and health: a structured expert judgement approach.

Introduction: Food insecurity is associated with increased risk for several health conditions and with poor chronic disease management. Key determinants for household food insecurity are income and food costs. Whereas short-term household incomes are likely to remain static, increased food prices would be a significant driver of food insecurity.

Fitness predicts long-term survival after a cardiovascular event: a prospective cohort study.

Objectives: To identify the role of fitness, fitness change, body mass index and other factors in predicting long-term (>5 years) survival in patients with coronary heart disease.

Design: Cohort study of patients with coronary heart disease recruited from 1 January 1993 to 31 December 2002, followed up to March 2011 (1 day to 18 years 3 months, mean 10.7 years).

Matching patients to an intervention for back pain: classifying patients using a latent class approach.

Rationale, Aims And Objectives: Classification of patients with back pain in order to inform treatments is a long-standing aim in medicine. We used latent class analysis (LCA) to classify patients with low back pain and investigate whether different classes responded differently to a cognitive behavioural intervention. The objective was to provide additional guidance on the use of cognitive behavioural therapy to both patients and clinicians.