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In medical decision making, distribution functions are used for two main purposes. The first is to model variability in data at the individual observation level (often subjects or patients). The second is to model uncertainty in the parameter estimates of decision models.

Distributions for Modeling Variability

Distributions that are used to model variability can be either discrete or continuous. Examples of discrete distributions include the binomial distribution, commonly used to model the occurrence or not of an event of interest from a total sample size, and the Poisson distribution, commonly used to model counts of events. Examples of continuous distributions that are used to model data variability are the normal distribution and gamma distribution. The modeling of variability is particularly important for discrete-event simulation (DES) models, which ...

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