Distributions in statistics are often used to describe the spread of values for a particular characteristic that is measured in a population (which may be an ‘input parameter’ for an economic evaluation). For example, although we may know the mean age of a population, nearly all individuals will have ages falling above or below this mean value, and not necessarily in a uniform manner. Parameter distributions are frequently defined using a mean and standard deviation (which fully define a normal distribution), or by means of “shape” and “scale” parameters for more complex distributions. Commonly used distributions In economic modelling are symmetrical, such as the normal distribution, often used for parameters such as population age and intervention effectiveness (e.g. relative risk reduction), or skewed, such as gamma or lognormal, for ratios or for parameters such as costs which cannot be negative. Distributions that describe a mutually exclusive set of outcomes, such as binomial, Poisson, beta or Dirichlet are used to represent input parameters that are probabilities. Specifying model input parameters as distributions (not just fixed values) enables probabilistic sensitivity analysis to be performed, allowing the uncertainty of the model outputs (e.g. incremental cost-effectiveness ratio) to be described and assessed.


How to cite: Distributions [online]. (2016). York; York Health Economics Consortium; 2016. https://yhec.co.uk/glossary/distributions/


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