Pharmacological Modelling (3 day course)
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3 day course. From Monday midday to Thursday midday: 11th - 14th February 2019.
Pharmacological models are used to describe the pharmacokinetics and pharmacodynamics of drug administration. The former concerns how the drug becomes distributed within the body and the latter how drug concentrations are related to physiological effects. The way in which models are derived from simplified representations of the body, and approaches to the estimation of model parameters, will be described. More advanced hierarchical models and Bayesian methods for population pharmacokinetics will be discussed, and their fitting using PROC NLMIXED in SAS described. The course will also cover the design and analysis of phase I clinical trials in human volunteers designed to establish a maximum safe dose and to identify indications of therapeutic activity. In particular, modern designs for dose-escalation based on Bayesian procedures will be studied.
Practicals will provide an opportunity for participants to fit simple models to data, and to design and evaluate phase I dose-escalation studies.
Important please note:
We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.
Details of course fees.
Payment: Once you have registered, please pay at the online shop
Accommodation Details: Can be found here.
Cancellation Policy
Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.
Programme:
Summarising pharmacokinetic data
Emax models and compartmental models
Hierarchical and Bayesian models
Designing PK studies
Models for repeated dosing
The uses of simulation
Dose escalation studies in healthy volunteers
Phase I/II studies
Non-attendance
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