Credible Interval
A credible interval is a Bayesian range that contains an unknown parameter, such as a treatment effect, with a stated posterior probability, often 95%.
Encyclopaedia
Explore comprehensive, evidence-informed explanations of key health economics concepts, including their development, application and relationships to other concepts. Published entries are validated through human expert review.
Concepts, entries and ontology terms
A concept page is a published entry on the wiki. Some concepts also have a full encyclopaedia article. The Health Economics Ontology (HEO) holds every concept, including dictionary terms without an article, so it is larger than the list of published pages. The knowledge graph shows how concepts relate to each other.
Credible Interval Estimation
The Bayesian process of calculating a range of plausible values for a parameter based on its estimated posterior probability distribution.
Critical Value
A critical value is a cutoff on a test statistic's reference distribution that defines a rejection boundary for a specified hypothesis test and error level.
Cross-Validation
Cross-validation estimates how well a prediction model, such as a cost or risk model, will perform on new data by testing it on data held out from fitting.
Cumulative Hazard
Cumulative hazard, H(t), is the hazard rate summed over time in survival models. Survival equals exp(-H(t)), and unlike a probability H(t) can exceed 1.
Cure Fraction
The cure fraction is the proportion of patients a survival model expects never to die from their disease, the level at which relative survival plateaus.
Cure Fraction Model
A cure fraction model is a survival model estimating the share of patients cured and the survival of the uncured, used to extrapolate survival in HTA.
Cure Model
A cure model is a survival model in which disease-related excess mortality falls to zero for a share of patients, so the survival curve levels off.
Cure Rate Model
A cure rate model is a survival model that estimates the cure fraction and survival of uncured patients, used in HTA to extrapolate long-term survival.
Curve Fitting
Curve fitting is estimating a mathematical function's parameters to match observed data, as when survival curves are fitted to trial data and extrapolated.
Cycle
A cycle is a defined time interval during which a discrete-time state-transition model updates health-state occupancy and accumulates relevant outcomes and costs.
Cycle Length
Cycle length is the duration of one cycle in a Markov or other discrete-time state-transition model, such as a month or a year, set before the model runs.
D-Efficient Design
A D-efficient design is a discrete choice experiment design chosen for low D-error, giving precise preference estimates under an assumed model.
Data Mining
Data mining is the computational search of large health datasets, such as claims or patient records, for patterns that were not specified in advance.
Deadweight Loss
Deadweight loss is the net loss of total surplus when a tax, subsidy, market power or insurance drives a wedge between price and marginal cost.
Decision Fatigue Concept
The decision fatigue concept holds that decision quality falls after a long run of choices, as when clinicians treat patients differently late in a shift.
Decision Maker
A decision maker is the person or body, such as NICE, whose choice an economic evaluation informs and whose objectives, budget and values it reflects.
Decision Node
A decision node is a point in a decision tree, drawn as a square, where the decision maker chooses between options and no probabilities are attached.
Decision Problem
A decision problem is the choice an economic evaluation or model must inform, stated as population, options compared, outcomes, costs and decision maker.
Decision Rule
A decision rule is an explicit criterion, such as an ICER threshold or net benefit, for choosing between healthcare interventions in economic evaluation.