Absorbing State
An absorbing state is a state in a Markov model that patients cannot leave once they enter it, usually death, so the whole cohort eventually ends there.
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.
Adaptive Value of Information
Adaptive value of information applies VOI analysis to staged research, re-valuing further data at each interim look to decide whether to continue.
Adverse Event
An adverse event (AE) is any unfavourable medical occurrence after treatment, caused by it or not; in patient safety, an injury caused by medical care.
Adverse Event Rate
Adverse event rate (AE rate) is the count of adverse events, or of patients with one, divided by the patients or person-time at risk in a stated period.
Agent-Based Model
An agent-based model (ABM) simulates individuals as agents that follow rules and interact, so outcomes such as infections, costs and QALYs emerge.
Akaike Information Criterion
Akaike information criterion (AIC) is a statistic ranking models fitted to the same data, such as survival curves in HTA, by fit penalised per parameter.
All-Cause Mortality
All-cause mortality is the rate of death from any cause in a population; models take background rates from life tables, and trials use it as an endpoint.
Antithetic Variates
Antithetic variates are a variance reduction method that pairs each random draw with its mirror image so errors tend to cancel in Monte Carlo simulation.
Applicability Assessment
Applicability assessment judges whether an economic evaluation or model fits a decision problem's population, comparators, care setting and methods.
Arrival Process
Arrival process is the pattern in time, random or scheduled, by which patients or other entities enter a queueing model or simulation of a health service.
Assumption Structure
Assumption structure is the full set of simplifying assumptions in a health economic model's design, such as its health states, events and extrapolation.
Background Mortality
Background mortality is the death rate a model's patients would face without the disease, usually taken from general population life tables by age and sex.
Base Case
The base case is a model's preferred set of inputs and assumptions and the primary results they produce, against which sensitivity analyses are compared.
Baseline Risk
Baseline risk is the probability of an event without the intervention under study; models apply relative treatment effects to it to give absolute benefit.
Bathtub Hazard
A bathtub hazard is a U-shaped hazard that is high soon after an event such as surgery, falls to a lower stable level, then rises again as patients age.
Bayesian Analysis CEA
Bayesian cost-effectiveness analysis updates prior evidence on costs and health effects with new data to estimate net benefit and decision uncertainty.
Bayesian Information Criterion
Bayesian information criterion (BIC) ranks models fitted to the same data, such as HTA survival curves, by fit with a penalty that grows with sample size.
Bayesian Optimisation
Bayesian optimisation seeks the inputs that optimise a costly function, such as a model's fit to calibration targets, using a surrogate to pick each run.
Beta Distribution
The beta distribution is a probability distribution on values between zero and one, used in decision models to represent uncertainty in probabilities.
Black Box Model
A black box model is a model whose workings cannot be inspected or understood, such as a locked cost-effectiveness model or a deep learning algorithm.