Limits...
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.


Dose selection plot. The exposure–response is depicted as change from baseline in λ at day 28 against average daily concentration. The blue elements are the model-based mean and 95% prediction interval generated from 500 simulations on a fine grid. The two-dimensional densities, built from patients observations, indicate the dose groups from the last conducted studies. A placebo group had been studied and a placebo effect observed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4150925&req=5

fig8: Dose selection plot. The exposure–response is depicted as change from baseline in λ at day 28 against average daily concentration. The blue elements are the model-based mean and 95% prediction interval generated from 500 simulations on a fine grid. The two-dimensional densities, built from patients observations, indicate the dose groups from the last conducted studies. A placebo group had been studied and a placebo effect observed.

Mentions: The impact of count modeling can take place at all phases of drug development, as shown by analyzed studies ranging from animal experiments56 to patient trials.9 Decisions that can be informed include go-nogo or dose selection. The function λ is where the drug effect, subject of the evaluation, is most likely to be implemented and a target response to be defined. Graphical57 representations should be sought (e.g., Figure 8) and parameter uncertainty considered when relevant. Drug development strategic questions generally involve several aspects such as efficacy and safety, in which case Gupta et al.8 combined a count model and a categorical model to predict the therapeutic index of a new formulation. When the different aspects concern the same end point, like frequency and severity of a unique type of events, models can be combined on another level,48 but this has not been done for count models yet.


Modeling and simulation of count data.

Plan EL - CPT Pharmacometrics Syst Pharmacol (2014)

Dose selection plot. The exposure–response is depicted as change from baseline in λ at day 28 against average daily concentration. The blue elements are the model-based mean and 95% prediction interval generated from 500 simulations on a fine grid. The two-dimensional densities, built from patients observations, indicate the dose groups from the last conducted studies. A placebo group had been studied and a placebo effect observed.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: Dose selection plot. The exposure–response is depicted as change from baseline in λ at day 28 against average daily concentration. The blue elements are the model-based mean and 95% prediction interval generated from 500 simulations on a fine grid. The two-dimensional densities, built from patients observations, indicate the dose groups from the last conducted studies. A placebo group had been studied and a placebo effect observed.
Mentions: The impact of count modeling can take place at all phases of drug development, as shown by analyzed studies ranging from animal experiments56 to patient trials.9 Decisions that can be informed include go-nogo or dose selection. The function λ is where the drug effect, subject of the evaluation, is most likely to be implemented and a target response to be defined. Graphical57 representations should be sought (e.g., Figure 8) and parameter uncertainty considered when relevant. Drug development strategic questions generally involve several aspects such as efficacy and safety, in which case Gupta et al.8 combined a count model and a categorical model to predict the therapeutic index of a new formulation. When the different aspects concern the same end point, like frequency and severity of a unique type of events, models can be combined on another level,48 but this has not been done for count models yet.

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.