Parametric refers to a broad classification of statistical procedures, including tests, which rely on assumptions about the ‘shape’ of the distribution of a measured characteristic of the underlying population, as well as the parameters used to describe that assumed distribution. A frequent assumption is that of an approximately normal distribution, described by its mean and standard deviation. Commonly used examples of parametric statistical procedures are t tests, analysis of variance (ANOVA) and all forms of regression. It is important to validate the assumptions associated with a parametric procedure, as incorrect conclusions can be made if the data deviate from these assumptions: in particular a parametric assumption of normality may be questionable for small sample sizes. In economic modelling parametric functions (such as Weibull, Gamma or exponential) are frequently used to represent overall survival or time to other important events such as disease progression or treatment discontinuation. These functions are used to project the experience of modelled cohorts beyond the duration of measured experience and can help in sensitivity analyses to assess the impact of parameter uncertainty on the model outputs.  Where data cannot be assumed to follow a specifically defined ‘shape’, then non-parametric tests should be used instead.

How to cite: Parametric (Tests) [online]. (2016). York; York Health Economics Consortium; 2016.


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