TOMORROW --- AAPS free webinar on covariate selection

Sponsored by the Population Pharmacokinetics and Pharmacodynamics Focus Group, American Association of Pharmaceutical Scientists *************************************************************************************** * Wednesday, Apr 14, 2010 from 12:30 - 2:00 pm EST * * * The Full Covariate Models and WAM Algorithm: *Efficient Building of Covariate Models and Appropriate Inferences about Covariate Effects * * * Conducted by * Marc R. Gastonguay, Ph.D., Metrum Research Group & Metrum Institute * Kenneth G. Kowalski, M.S., Ann Arbor Pharmacometrics Group * Moderated by * Liping Zhang, Ph.D., Bristol-Myers Squibb * *************************************************************************************** Register 15 min before it starts, at https://www2.gotomeeting.com/register/126533363, After the registration an email with link to logon will be sent to you. Clink the link to join the webinar. The Q&A Session will follow the formal lecture at approximately 1:15 pm EST. You may ask questions at any time during the webinar by typing them in to the question box on your screen. You may also ask questions in advance during the registration process. PDF Handouts: you may download the presenter's handouts at: http://mediaserver.aapspharmaceutica.com/meetings/webinars/ppdm-4/ppdm-4.pdf Firewall or Other Connection Issues? Please visit the following site for troubleshooting information: https://www1.gotomeeting.com/default/help/g2m/troubleshooting/connection_test_help.htm Past Problems Logging In? Here is a URL which will help you connect to our webinars: http://www.gotomeeting.com/wizard Description: This presentation will provide an overview of Full Covariate Modeling method (Gastonguay, 2004) and Wald's Approximation Method (WAM) for covariate model building based on the methodology of Kowalski and Hutmacher (2001). An example will be used to illustrate application of the WAM algorithm and the value obtained from evaluating all reduced models among the complete set of possible hierarchical covariate models leading to a parsimonious final model. The presentation will also illustrate the utility of the Full Covariate Model approach for inference about covariate effects and some possible strategies for the successful development of a full model necessary to employ the WAM approach and subsequent model-based simulation goals. The presentation will highlight the advantages and disadvantages of the Full Covariate Model approach and WAM approach and in comparison to standard stepwise procedures. The presentation will conclude with a discussion of the available software to implement the WAM algorithm and Full Covariate Model. The WAM algorithm was first published in 2001; however, it has not been widely used in the modeling community in part because it requires the development of a full model. At the time of its introduction, development of a full model in which all covariate effects are estimated simultaneously was not routinely performed. Today, there is a greater appreciation of the value in developing full models, particularly for inferential purposes regarding covariate effects. This webinar will present two promising approaches for covariate model building that leverage information from a full model as alternatives to standard stepwise procedures. Goals and Objectives: * To provide an overview of the goals of covariate modeling in the drug development process; * To provide an overview of Full Covariate Model and WAM algorithm approaches relative to traditional Stepwise Regression, define advantages and disadvantages of each method as a function of covariate modeling objectives in drug development; * To promote an awareness of the methodologies and the available software. ________________________________ This message (including any attachments) may contain confidential, proprietary, privileged and/or private information. The information is intended to be for the use of the individual or entity designated above. If you are not the intended recipient of this message, please notify the sender immediately, and delete the message and any attachments. Any disclosure, reproduction, distribution or other use of this message or any attachments by an individual or entity other than the intended recipient is prohibited.

Sponsored by the Population Pharmacokinetics and Pharmacodynamics Focus Group, American Association of Pharmaceutical Scientists *************************************************************************************** * Wednesday, Apr 14, 2010 from 12:30 - 2:00 pm EST * * * The Full Covariate Models and WAM Algorithm: *Efficient Building of Covariate Models and Appropriate Inferences about Covariate Effects * * * Conducted by * Marc R. Gastonguay, Ph.D., Metrum Research Group & Metrum Institute * Kenneth G. Kowalski, M.S., Ann Arbor Pharmacometrics Group * Moderated by * Liping Zhang, Ph.D., Bristol-Myers Squibb * *************************************************************************************** Register 15 min before it starts, at https://www2.gotomeeting.com/register/126533363, After the registration an email with link to logon will be sent to you. Clink the link to join the webinar. The Q&A Session will follow the formal lecture at approximately 1:15 pm EST. You may ask questions at any time during the webinar by typing them in to the question box on your screen. You may also ask questions in advance during the registration process. PDF Handouts: you may download the presenter's handouts at: http://mediaserver.aapspharmaceutica.com/meetings/webinars/ppdm-4/ppdm-4.pdf Firewall or Other Connection Issues? Please visit the following site for troubleshooting information: https://www1.gotomeeting.com/default/help/g2m/troubleshooting/connection_test_help.htm Past Problems Logging In? Here is a URL which will help you connect to our webinars: http://www.gotomeeting.com/wizard Description: This presentation will provide an overview of Full Covariate Modeling method (Gastonguay, 2004) and Wald's Approximation Method (WAM) for covariate model building based on the methodology of Kowalski and Hutmacher (2001). An example will be used to illustrate application of the WAM algorithm and the value obtained from evaluating all reduced models among the complete set of possible hierarchical covariate models leading to a parsimonious final model. The presentation will also illustrate the utility of the Full Covariate Model approach for inference about covariate effects and some possible strategies for the successful development of a full model necessary to employ the WAM approach and subsequent model-based simulation goals. The presentation will highlight the advantages and disadvantages of the Full Covariate Model approach and WAM approach and in comparison to standard stepwise procedures. The presentation will conclude with a discussion of the available software to implement the WAM algorithm and Full Covariate Model. The WAM algorithm was first published in 2001; however, it has not been widely used in the modeling community in part because it requires the development of a full model. At the time of its introduction, development of a full model in which all covariate effects are estimated simultaneously was not routinely performed. Today, there is a greater appreciation of the value in developing full models, particularly for inferential purposes regarding covariate effects. This webinar will present two promising approaches for covariate model building that leverage information from a full model as alternatives to standard stepwise procedures. Goals and Objectives: * To provide an overview of the goals of covariate modeling in the drug development process; * To provide an overview of Full Covariate Model and WAM algorithm approaches relative to traditional Stepwise Regression, define advantages and disadvantages of each method as a function of covariate modeling objectives in drug development; * To promote an awareness of the methodologies and the available software. ________________________________ This message (including any attachments) may contain confidential, proprietary, privileged and/or private information. The information is intended to be for the use of the individual or entity designated above. If you are not the intended recipient of this message, please notify the sender immediately, and delete the message and any attachments. Any disclosure, reproduction, distribution or other use of this message or any attachments by an individual or entity other than the intended recipient is prohibited.