Overfitting
A situation in which a model is fit too closely to the noise in its training data, performing poorly on new, independent data.
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A situation in which a model is fit too closely to the noise in its training data, performing poorly on new, independent data.
A statistic representing the probability of observing a result at least as extreme as the one obtained, assuming the null hypothesis is true.
A comparison of exactly two interventions at a time, used as a building block within a fully incremental analysis of several options.
A missing data method in which each specific calculation uses all observations with complete data for that particular pair of variables.
The specific probability distribution, such as a beta, gamma, or normal distribution, assigned to represent uncertainty in a given input parameter.
The statistical process of using observed data to determine the most likely values for a model's unknown parameters.
Uncertainty arising from imperfect knowledge of the true value of an input parameter, such as a transition probability or unit cost.
A statistical approach estimating a survival function by assuming the data follow a specific distribution, such as Weibull or log-normal, with estimated parameters.
Survival estimates derived from a fitted mathematical distribution rather than directly from observed data alone, allowing extrapolation beyond the data.
A statistical model assuming time-to-event data follow a specific distribution, such as Weibull or log-normal, allowing extrapolation beyond observed data.
A state of resource allocation in which no individual can be made better off without making at least one other individual worse off.
The set of allocations or outcomes for which no alternative exists that would improve one party's position without worsening another's.
A change in resource allocation that makes at least one individual better off without making any other individual worse off.
The contribution of a single attribute level to an individual's overall utility for a good, estimated from a conjoint or choice experiment.
A measure of the relationship between two variables after statistically controlling for the influence of one or more additional variables.
A study examining either the costs or the outcomes of an intervention, or both without comparison to an alternative, short of a full economic evaluation.
A Monte Carlo technique estimating a dynamic system's state over time using weighted sample points, called particles, updated as new data arrive.
A population-based optimisation technique inspired by flocking behaviour, in which candidate solutions adjust position based on their own and the group's best results.
An oncology modelling technique estimating the proportion of a cohort in each health state using separately fitted survival curves for different endpoints, rather than modelling transitions.
A missing data modelling approach stratifying analysis by the observed pattern of missingness, rather than assuming one model applies to everyone.