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Modeling and simulation of count data.

Plan EL - CPT Pharmacometrics Syst Pharmacol (2014)

Bottom Line: Count data, or number of events per time interval, are discrete data arising from repeated time to event observations.Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family.Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden [2] Pharmetheus, Uppsala, Sweden.

ABSTRACT
Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

No MeSH data available.


Representation of count data. The left panel displays the time course of the counts in a 25-individual population, whereas the right panel reveals the probability mass function of the counts in the same population.
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fig1: Representation of count data. The left panel displays the time course of the counts in a 25-individual population, whereas the right panel reveals the probability mass function of the counts in the same population.

Mentions: There are several—standard or not—ways to visualize count data, and a representative sample will be given in this tutorial. The familiar representation of the dependent variable—here the counts—vs. time (Figure 1, left panel) is inevitable, although it does not carry the notion of the integer nature of the observations. This type of graph will, as for other data, inform about the variability between subjects as well as a potential time course.


Modeling and simulation of count data.

Plan EL - CPT Pharmacometrics Syst Pharmacol (2014)

Representation of count data. The left panel displays the time course of the counts in a 25-individual population, whereas the right panel reveals the probability mass function of the counts in the same population.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150925&req=5

fig1: Representation of count data. The left panel displays the time course of the counts in a 25-individual population, whereas the right panel reveals the probability mass function of the counts in the same population.
Mentions: There are several—standard or not—ways to visualize count data, and a representative sample will be given in this tutorial. The familiar representation of the dependent variable—here the counts—vs. time (Figure 1, left panel) is inevitable, although it does not carry the notion of the integer nature of the observations. This type of graph will, as for other data, inform about the variability between subjects as well as a potential time course.

Bottom Line: Count data, or number of events per time interval, are discrete data arising from repeated time to event observations.Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family.Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden [2] Pharmetheus, Uppsala, Sweden.

ABSTRACT
Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

No MeSH data available.