Bill Gillespie, Ph.D., gave a presentation on "Torsten: Stan functions for pharmacometrics applications" at the "Stan for Phamacometrics" event in Paris, France on July 24, 2018. You can view the presentation here.
William Gillespie, Ph.D. presented at the Midwest Biopharmaceutical Statistics Workshop on May 15, 2018. See presentation here.
Poster presented by Alanna S. Ocampo-Pelland and Jonathan L. French at ACoP8. See the poster here.
by Tim Bergsma
Already widely received in the finance community, cloud computing is gaining acceptance in the pharmaceutical industry as well. At this year’s American Conference on Pharmacometrics, Metrum Research Group staff presented a poster on the topic, and helped lead a related panel discussion.
For new developers, getting a package ready for building and submitting to CRAN is an expletive-filled, head-scratching experience to say the least. Trying to figure out the basics of what goes in depends and what goes in imports is a lost afternoon most of us would like back. Once that is understood, filling in relevant information to each field is a mundane task even for a well polished package developer. The out-of-the-box roxygen skeleton supplied by RStudio gives the bare bones road map of what should be part of function documentation:
Recently, I was asked to speak at this year’s PaSiPhIC conference about different approaches to meta-analysis. As I was putting together my presentation, I began to wonder: How can we best leverage traditional meta-analysis methods in a model-based drug development framework?
The primary rationale for model-based meta-analysis (MBMA) is to improve decision-making by better leveraging prior information from multiple sources. Decision-makers generally attempt to consider such prior information, but it is usually done in a relatively qualitative manner, and each individual decision-maker is usually aware of only a subset of the prior information. MBMA seeks to make the process more quantitative and comprehensive. The process and results of MBMA may be made visible (aka transparent) to the decision-makers. The end result is that the decision-makers are better informed, and they can contribute their knowledge to the modeling process leading to better, more trusted models and model-based inferences.
I recently returned from the 2013 PAGE meeting in Glasgow. As usual, the scientific presentations were some of the best in the field of pharmacometrics. At this year’s meeting I was offered an opportunity to present some of our recent thoughts about model-based drug development in oncology.