Dear colleagues,
We are pleased to offer 2 courses “Advanced methods for population model
building, evaluation and usage in NONMEM” and “Pharmacometric modeling of
composite score outcomes” in Lambertville, New Jersey March 9-11 and March
12-13, 2020. The two courses will also be given in Uppsala, Sweden on May 11-13
and May 14-15, respectively. For more information and registration see below
and at http://www.uppsala-pharmacometrics.com.
Advanced methods for population model building, evaluation and usage in NONMEM
Pharmacometric modeling has become a pillar in model informed drug development
(MIDD). With this comes expectations with respect to quality, efficiency,
transparency and innovation in the implementation of the modeling and
decision-making process. In this course we will present methods that will help
make model building of standard problems more efficient and improve the final
product. Further, it will give modelers a larger toolset of diagnostics and
model components when it comes to development of models for challenging
situations. Automated procedures recently developed for PsN & R facilitates a
comprehensive assessment of a model and tailored functionality allow
command-line transformations of models.
Mats Karlsson and Andrew Hooker will give a 2.5-day course on “Advanced methods
for population model building, evaluation and usage in NONMEM”. The course
presents strategies for model building and improvement, the latest methods for
model evaluation, as well as strategies to consider when utilizing models for
model-informed drug development.
The course consists of both lectures and hands-on computer exercises applying
the methods discussed. This hands-on material is based on the most recent
developments from NONMEM 7.4, PsN and Xpose. Participants get a vast amount of
hands-on examples, code, code snippets and lecture material that can be useful
on a daily basis.
If you want to learn how to use tools and methods for fast, efficient and
comprehensive model building, evaluation and usage, come join us in March!
Topics covered:
* Model building and model components
* Overall modeling strategies
* Random effects models (standard and extended)
* Residual error models (standard and extended)
* Mixture modeling
* Handling censored data (e.g. BQL and dropout)
* Covariate models and model building
* Estimation methods and settings
* Model evaluation
* Prediction- and Residual-based
* Empirical Bayes Estimate (EBE) and sampling-based diagnostics
* Simulation and Simulation-Evaluation/Estimation-Based
* Outlier and influential individual diagnostics
* Automated evaluations
* Covariate model focused diagnostics
* Parameter uncertainty (bootstrap, SIR, COV)
* To consider when applying models for informed drug development
* Bias assessment
* Power and Type I error
* Model averaging
Pharmacometric modeling of composite score outcomes
The EDSS in multiple sclerosis, the ACR scale for rheumatoid arthritis, or the
MDS-UPDRS in Parkinson’s disease; these composite scales are as diverse as the
diseases they are designed to measure. Composite scores also arise through
patient-reported outcomes (PROs) that aim at measuring symptom status, physical
function, mental health, and other measures important to patients. The wide
range of novel pharmacometric models and approaches developed during the past
years are a testament to the growing importance of this data type. This course
will cover recent innovations and provide participants with a rich set of tools
for analyzing composite scores in a wide variety of therapeutic areas. The
course will illustrate how one can leverage finely grained item-level
information but also how summary score data are most efficiently utilized.
Throughout the course, questions of both model building and model-use will be
discussed and presented in an interactive format including hands-on exercises.
Mats Karlsson and Sebastian Ueckert will give a 2-day course on “Pharmacometric
modeling of composite score outcomes”. The course covers data aspects, model
building, evaluation and use for composite score outcomes.
Topics covered:
* Modeling data with item-level resolution using item response theory
* Modeling total score data either as continuous or discrete data under a
variety of models
* Models for responder analysis to handle, e.g., ACR20/50/70 and PASI70/90
* Pharmacometric modeling of Patient Reported Outcomes (PROs).
* Model-based optimization of clinical trials with composite score outcomes
Intended course participants:
The course is designed for those who have a good working knowledge of
pharmacometric analysis with experience in performing NONMEM analyses and/or
have attended a NONMEM basic workshop.
Best regards,
Mats Karlsson
När du har kontakt med oss på Uppsala universitet med e-post så innebär det att
vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du
läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/
E-mailing Uppsala University means that we will process your personal data. For
more information on how this is performed, please read here:
http://www.uu.se/en/about-uu/data-protection-policy
2 Advanced NONMEM courses given in both US and Europe in 2020
Dear colleagues,
We are pleased to offer 2 courses
“Advanced methods for population model building, evaluation and usage in
NONMEM” and “Pharmacometric modeling of composite score outcomes” in
Lambertville, New Jersey, March 9-11 and March 12-13, 2020. The two courses
will also be given in Uppsala, Sweden on May 11-13 and May 14-15, respectively.
For more information and registration see below and at
http://www.uppsala-pharmacometrics.com.
Advanced methods for population model building, evaluation and usage in NONMEM
Pharmacometric modeling has become a pillar in model informed drug development
(MIDD). With this comes expectations with respect to quality, efficiency,
transparency and innovation in the implementation of the modeling and
decision-making process. In this course we will present methods that will help
make model building of standard problems more efficient and improve the final
product. Further, it will give modelers a larger toolset of diagnostics and
model components when it comes to development of models for challenging
situations. Automated procedures recently developed for PsN & R facilitates a
comprehensive assessment of a model and tailored functionality allow
command-line transformations of models.
Mats Karlsson and Andrew Hooker will give a 2.5-day course on “Advanced methods
for population model building, evaluation and usage in NONMEM”. The course
presents strategies for model building and improvement, the latest methods for
model evaluation, as well as strategies to consider when utilizing models for
model-informed drug development.
The course consists of both lectures and hands-on computer exercises applying
the methods discussed. This hands-on material is based on the most recent
developments from NONMEM 7.4, PsN and Xpose. Participants get a vast amount of
hands-on examples, code, code snippets and lecture material that can be useful
on a daily basis.
If you want to learn how to use tools and methods for fast, efficient and
comprehensive model building, evaluation and usage, come join us in March!
Topics covered:
* Model building and model components
* Overall modeling strategies
* Random effects models (standard and extended)
* Residual error models (standard and extended)
* Mixture modeling
* Handling censored data (e.g. BQL and dropout)
* Covariate models and model building
* Estimation methods and settings
* Model evaluation
* Prediction- and Residual-based
* Empirical Bayes Estimate (EBE) and sampling-based diagnostics
* Simulation and Simulation-Evaluation/Estimation-Based
* Outlier and influential individual diagnostics
* Automated evaluations
* Covariate model focused diagnostics
* Parameter uncertainty (bootstrap, SIR, COV)
* To consider when applying models for informed drug development
* Bias assessment
* Power and Type I error
* Model averaging
Pharmacometric modeling of composite score outcomes
The EDSS in multiple sclerosis, the ACR scale for rheumatoid arthritis, or the
MDS-UPDRS in Parkinson’s disease; these composite scales are as diverse as the
diseases they are designed to measure. Composite scores also arise through
patient-reported outcomes (PROs) that aim at measuring symptom status, physical
function, mental health, and other measures important to patients. The wide
range of novel pharmacometric models and approaches developed during the past
years are a testament to the growing importance of this data type. This course
will cover recent innovations and provide participants with a rich set of tools
for analyzing composite scores in a wide variety of therapeutic areas. The
course will illustrate how one can leverage finely grained item-level
information but also how summary score data are most efficiently utilized.
Throughout the course, questions of both model building and model-use will be
discussed and presented in an interactive format including hands-on exercises.
Mats Karlsson and Sebastian Ueckert will give a 2-day course on “Pharmacometric
modeling of composite score outcomes”. The course covers data aspects, model
building, evaluation and use for composite score outcomes.
Topics covered:
* Modeling data with item-level resolution using item response theory
* Modeling total score data either as continuous or discrete data under a
variety of models
* Models for responder analysis to handle, e.g., ACR20/50/70 and PASI70/90
* Pharmacometric modeling of Patient Reported Outcomes (PROs).
* Model-based optimization of clinical trials with composite score outcomes
Intended course participants:
The course is designed for those who have a good working knowledge of
pharmacometric analysis with experience in performing NONMEM analyses and/or
have attended a NONMEM basic workshop.
Best regards,
Mats Karlsson
När du har kontakt med oss på Uppsala universitet med e-post så innebär det att
vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du
läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/
E-mailing Uppsala University means that we will process your personal data. For
more information on how this is performed, please read here:
http://www.uu.se/en/about-uu/data-protection-policy