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PRODID:-//Metrum Research Group - ECPv4.6.1//NONSGML v1.0//EN
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METHOD:PUBLISH
X-WR-CALNAME:Metrum Research Group
X-ORIGINAL-URL:https://metrumrg.com
X-WR-CALDESC:Events for Metrum Research Group
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20181007T080000
DTEND;TZID=UTC+0:20181007T170000
DTSTAMP:20180718T003048
CREATED:20180405T175103Z
LAST-MODIFIED:20180522T142927Z
UID:1688-1538899200-1538931600@metrumrg.com
SUMMARY:Bayesian analysis of categorical\, count and time-to-event data with Stan\, rstanarm and Torsten
DESCRIPTION:We will provide a guided hands-on experience in the use of Stan (http://mc-stan.org/)\, rstan (http://mc-stan.org/interfaces/rstan.html)\, rstanarm (http://mc-stan.org/users/interfaces/rstanarm.html) and Torsten (https://github.com/metrumresearchgroup/example-models) for Bayesian analysis of categorical\, count and time-to-event data. Stan is a flexible open-source software tool for Bayesian data analysis using Hamiltonian Monte Carlo (HMC) simulation—a type of MCMC simulation. Torsten is a Stan extension containing a library of functions to simplify implementation of PKPD models. This workshop builds on the foundations presented in our previous introductory Stan workshops (http://metrumrg.com/events/2016/10/23/bayesianpkpd.html). The hands-on examples will focus on implementation of Stan models for each of the basic data types covered in the workshop\, i.e.\, binary\, ordinal\, count and time-to-event data. We will also present examples that illustrate how those component models may be extended and combined for joint analysis of multiple data types. Those examples include latent variable models such as IRT models as well as joint modeling of continuous and time-to-event data. \nOctober 7\, 2017 8:00am-5:00pm (1.5 day ACoP9 satellite workshop) \nCourse Instructor: Bill Gillespie \nTopic Outline of Workshop: \nStart: 8:00am \n\nSome general theory/background:\n\nModeling from a probabilistic point of view: the likelihood function\nMaximum likelihood for continuous data\nExtending ML to odd-type data\nBayesian modeling of odd-type data\n\n\nBrief review of the use of Stan and rstan\n\nUser-defined functions\nModeling workflow using rstan\n\n\nModeling binary data\n\nLogistic regression models\nBernoulli model for individual binary data\nBinomial model for summary data\nMixed effects modeling of longitudinal binary data\n\n\n\nBreak: 10:15-10:30am \n\nModeling ordered categorical (ordinal) data\n\nCumulative logit models\n\n\nModeling count data\n\nThe Poisson model\nVariations on the Poisson model to deal with over-dispersion or zero inflation\n\n\n\nLunch: 12:00-1:00pm \n\nModeling time-to-event data for a single event per individual\n\nPrinciples and methods of survival analysis for modeling censored data\n\n\nTorsten: Library of PKPD functions for Stan\n\nBuilt-in models: 1 and 2 compartment models with 1st order absorption\nNumerical solution of user-specified ODEs\n\n\nModels with time-varying hazard\n\nBreak: 3:00-3:15pm \n\nModeling repeated time-to-event data\nMarkov models for joint modeling of event times and their magnitudes\nItem response theory (IRT) and related models for joint modeling of multiple outcomes\nJoint modeling of continuous and time-to-event data\n\nAdjourn: 5:00pm \nMaterials Provided by Sponsor: Course slides\, data and code for all examples\, online access for one week to a cloud-based compute server on which the software used in the course is installed \nRequirements for Attendees: Knowledge and experience in population PKPD modeling and the use of R and Stan. The workshop assumes a knowledge of Stan comparable to the content of our previous introductory workshops (http://metrumrg.com/events/2016/10/23/bayesianpkpd.html). For those who do not yet have the required knowledge of Stan we offer a recorded online version of an introductory workshop (http://metrumrg.com/course/brief-introduction-bayesian-modeling-using-stan/). \nParticipants should bring a laptop on which the Google Chrome browser has been installed and that may access the internet via Wi-Fi. \nPricing:\n\nIndustry $700\nAcademia/Gov/NonProfit $400\nStudent $250\n\n
URL:https://metrumrg.com/event/bayesian-analysis-categorical-count-time-event-data-stan-rstanarm-torsten/
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20181011T130000
DTEND;TZID=UTC+0:20181012T170000
DTSTAMP:20180718T003048
CREATED:20180405T175031Z
LAST-MODIFIED:20180522T143040Z
UID:1683-1539262800-1539363600@metrumrg.com
SUMMARY:Introduction to PBPK modeling in R with mrgsolve
DESCRIPTION:This hands-on workshop will provide participants with a technical foundation for physiologically-based pharmacokinetic (PBPK) model development in R using mrgsolve\, a free\, open-source modeling and simulation platform for ODE-based model development in R (https://mrgsolve.github.io). The workshop gets started with an introduction to simulation with mrgsolve. We’ll cover the essentials of a tidy simulation workflow in R. Then we’ll jump into PBPK modeling with mrgsolve. After a brief introduction to principles of PBPK\, we’ll work through case studies using PBPK modeling. The case studies use published models focused on problems like drug-drug interactions and exploring pharmacokinetics in special populations. Finally\, we will introduce tools and workflows in R to help you accomplish modeling tasks such as sensitivity analysis and parameter estimation. \n \nOctober 11-12\, 2017 (1.5 day ACoP9 satellite workshop)\n \nCourse Instructors: Ahmed Elmokadem\, Ph.D. and Kyle Baron\, Pharm.D.\, Ph.D. \nTopic outline of Workshop:\n \n\nPBPK concepts\n\nWhy PBPK?\nTypes of PBPK models (flow-limited and permeability-limited)\nDevelopment of model equations\nDatabases for PBPK model parameters\n\n\nIntroduction to mrgsolve for PBPK modeling\n\nA tidy PBPK model-based simulation workflow in R\nImplement dosing events\nCustomize simulation output\nApproaches to sensitivity analysis with mrgsolve\nModel specification basics\n\n\nCase studies in PBPK modeling\n\nStatin/cyclosporine DDI model (Yoshikado et al. 2016\, PMID 27170342)\nPediatric voriconazole PBPK model (Zane and Thakker 2014\, PMID 25245942)\n\n\nPBPK modeling workflow in R\n\nSensitivity analyses\n\nExploratory simulation\nLocal sensitivity analysis\nGlobal sensitivity analysis via sobol method\n\n\nParameter estimation \n\nGeneral optimization in R\nGlobal search tools\n\n\n\n\nDiscussion and wrap-up\n\nRequirements for Attendees: Knowledge and experience in PK modeling and the use of R (or S-PLUS). Previous exposure to the mrgsolve simulation package (https://mrgsolve.github.io/) is helpful\, but not required. \nMaterials Provided by Sponsor: Course slides\, data and code for all examples\, online access for one week to a cloud-based compute server on which the software used in the course is installed. \nPricing:\nIndustry $1\,050\nAcademia/Gov/NonProfit $600\nStudent $375\n
URL:https://metrumrg.com/event/introduction-pbpk-modeling-r-mrgsolve/
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