Knowledge database

Description

PK/PD (Pharmacokinetic/Pharmacodynamic) modeling is a technique that combines the pharmacologic disciplines of pharmacokinetics and pharmacodynamics. ModelRisk has a special PK/PD module that allows the incorporation of parameter uncertainty and individual variability into such models. This short demo will show you how it works and the benefits it brings.

PK/PD (Pharmacokinetic/Pharmacodynamic) modeling is a technique that combines the pharmacologic disciplines of pharmacokinetics and pharmacodynamics. ModelRisk has a special PK/PD module that allows the incorporation of parameter uncertainty and individual variability into such models. This short demo will show you how it works and the benefits it brings.

Target Audience

Pharmacy

Description

Correlation is a critical component of a risk analysis model in any field. Failure to correctly model the correlation between uncertain variables will give misleading, and sometimes dangerously wrong, results yet it receives very little attention. Most risk analysis software products provide only the most basic correlation tools. ModelRisk is very different. This short demo explains the correlation tools available in ModelRisk, how they work, and why they can make all the difference to your risk analysis.

Target Audience

Pharmacy

Description

ModelRisk has the capability to connect to a wide variety of databases, allowing you to build risk analysis models that use the most up-to-date data automatically. This short demo explains how the database tools work, and how you can build concise, adaptable models that always stay current.

Target Audience

Pharmacy

Description

It is very common in many fields of risk analysis to fit distributions to data. With well over 100 distributions available in ModelRisk, you will often be able to choose a distribution that fits your data well. The most common problem in fitting to data is that the bulk of the data fit nicely, but the level of fit is poor in the high-end tail. ModelRisk has a tool that allows you to ‘splice’ two distributions together and fit the combination to your data, ensuring that you get a good representation of the entire range of possible outcomes. This short demo shows how this tool works.

Description

Karl Luce documents the important role Monte Carlo Analysis (MCA) provides for Tolerance Analysis. Using the example of an overrunning one-way clutch, a paper and associated MS Excel Workbook Karl introduces us into the world of Tolerance Analysis. Content:

• Identifying assembly outputs (related to customer satisfaction) and their Specification Limits.
• Relating part dimensional tolerances to expected normal probability distributions.
• Performing MCA using ModelRisk on a Tolerance Analysis model.
• Using MCA results to guide future design decisions.
• Optimizing design decisions to meet multiple output requirements.

Description

This webinar will illustrate how to review historic data and use your knowledge of the system you are modeling to create more robust, credible forecasts that incorporate appropriate levels of uncertainty.

The webinar explains how to go about reviewing your data and the statistical tools and techniques that you can help reveal the underlying patterns. We will focus most on the thinking that underpins time series forecasting, using three different examples as illustration. Areas covered include:

• How far forward you can forecast with a data set
• Assessing the stability of past data and its relevance to a forecast
• Visual review of past data
• Assessing and modeling correlations between variable
• Pearson v Spearman correlation
• Forecasting actual values, log returns or simple returns
• Review of some common financial model
• Fitting distributions to movements
• Building dependent forecasts using regression
• Incorporating seasonality
• Evaluating and incorporating lags (i.e. early indicators)
• Adding bounds to forecast variables
• Assessing and adding shocks to the system
• Random event frequency modeling
• Reliability of a system (modelling lifetime and failures)