Metrum Research Group is proud to continue supporting training, education, and open courseware efforts in the field of pharmacometrics. MetrumRG is now hosting all course materials from the Metrum Institute training series. Course materials are freely available under Creative Commons Attribution 3 (http://creativecommons.org/licenses/by/3.0/).
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MI205 covers introductory through intermediate-level R programming topics with a focus on pharmacometric applications through lectures and hands-on lab sessions.
MI210 provides an extensive overview of topics in population pharmacokinetics and pharmacodynamics, including nonlinear mixed-effects modeling theory and implementation, data formatting requirements, population model development, model evaluation techniques, continuous PK-PD models, Monte Carlo simulation, and best practices. Instruction combines didactic lectures with hands-on exercises using R, MIfuns, and the NONMEM® 7 software.
MI212 covers intermediate through advanced-level population PK-PD modeling and simulation through lecture and hands-on lab sessions. Topics covered include for nonlinear PK models, modeling PK data with BQL records, models for parent-metabolite data, models for plasma and urine PK data, indirect PK-PD models, disease progression models and clinical trial simulations. This course makes extensive use of NONMEM® 7 and R, as well as the MIfuns package.
MI250 provides an introduction to Bayesian modeling theory and the practical use of WinBUGS and R for PK-PD applications. In addition to basic concepts, this course includes instruction on BUGSModelLibrary, an open-source tool developed by Metrum Institute. This library facilitates the implementation of population PK-PD models in WinBUGS for compartmental models described by algebraic or differential equations.
MI255 is an intensive course providing an introduction to modeling of categorical, count, and time-to-event data, and the practical use of WinBUGS and NONMEM® for such applications. The course provides some basic theory and illustrates some of the advantages of using Bayesian methods for these types of data sets.
MI260 provides an introduction to meta analysis concepts and methods, with a strong focus on model-based meta-analysis of summary data or a combination of summary and individual data from clinical trials to support decision-making in clinical drug development. Upon completion of the course, participants will be able to write a meta-analysis plan, design a model-based meta-analysis of clinical trial data to address strategic decisions in a clinical drug development program, and implement it using a Bayesian approach executed with WinBUGS and R. Participants will also be able to construct a model for the relationship between an efficacy- or safety-related clinical outcome and independent variables such as dose, time and patient characteristics by analysis of summary data from multiple studies, e.g., treatment means and standard deviations, and construct such a model by analysis of a combination of summary and individual data, as well as execute and interpret population simulations to support decision-making in clinical drug development.