- 2018 Pharmacometric Programming Webinar:
The American Statistical Association (ASA) Section for Statistical Programmers and Analysts (SSPA) and the Statistics and Pharmacometrics Interest Group (SxP) jointly sponsored a webinar on Pharmacometric Programming. The webinar will take place on Tuesday, Jan 30th from 12:00-1:30 pm. The description and registration instructions are listed below. Please note that multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).
Title: Pharmacometric Programming
Presenters: Amit Roy, Jing Su, Neelima Thanneer, and Jeffry Florian
Date and Time: Tuesday, January 30, 2018, 12:00 p.m. – 1:30 p.m. Eastern time
Registration Deadline: Friday, January 26, at 12:00 p.m. Eastern time
ASA Joint Statistical Meeting (JSM) 2017: July 29 – August 3 2017 in Baltimore, WA. Topic contributed papers by our interest group on Pharmacometric Programming: Thu. August 3rd, 2017, 8:30 AM - 10:20 AM
Clinical Pharmacology Workshop Series 2017: August 9 – 11 2017 in Salt Lake City, Utah, USA. By University of Utah, Department of Pediatrics, Division of Clinical Pharmacology.
CENISBS: Joint Conference on Biometrics & Biopharmaceutical Statistics: 28 August – 1 September 2017 in Vienna, Austria
PODE: 12th Workshop on Population Optimum Design of Experiments (PODE) 2017, Friday, 8 September 2017, the Biostatistics and Pharmacometrics department of Novartis Pharma.
One of the highlights will be our Steering Committee Member, Jose Pinheiro's talk, on “Exposure-response modeling for dose selection under model uncertainty: Extending the MCP-Mod approach”. This goes beyond the typical PK sampling time point optimization and exemplifies the relationship between study design aspects and optimal design considerations.
SxP Proposed session accepted by ACoP8 (October 15-18, 2017 in Fort Lauderdale, FA, USA): Integrating quantitative disciplines - Making model-informed discovery and drug development (MID3) work in practice, proposed by Mark K. Smith (Pfizer).
To truly deliver model-informed discovery and drug development (MID3), we need to integrate information across a wide range of quantitative disciplines - systems pharmacology, clinical pharmacology, pharmacometrics, statistics, epidemiology, decision scientists. Yet there is a danger that each discipline works in isolation, developing models and making inferences. Models and information sharing struggles to cross between quantitative groups. Assumptions are not well captured, articulated or shared in ways that make model scope and limitations clear. Instead of accumulating knowledge and understanding across the quantitative groups, we get mistrust, rework and a myopic view. But by embracing the principles of MID3, employing good practice, and finding suitable ways to share and build on knowledge which accumulates across disciplines we can work more effectively to bring a complete picture of drug action to development of new medicines and drug regimens, and to use that information to plan and design efficient studies. In this session we will look at how the quantitative disciplines might work together effectively, break down silos, work together on models, effectively share information, inform design, make better inferences and decisions.