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This is the preliminary program for the 2009 Joint Statistical
Meetings in Washington, DC.
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The views expressed here are those of the individual authors and not necessarily those of the ASA or its board, officers, or staff. Back to main JSM 2009 Program page |
= Applied Session,
= Theme Session,
= Presenter| CE_21C | Mon, 8/3/09, 1:00 PM - 5:00 PM | RH-Meeting Rooms 12, 13, 14 |
| Evaluating Probability of Success for Internal Decisionmaking in Early Drug Development - Continuing Education - Course | ||
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ASA, Biopharmaceutical Section |
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| Instructor(s): Martin King, Abbott Laboratories, Narinder Nangia, Abbott Laboratories, Jane Qian, Abbott Laboratories | ||
| Early development (the "learning stage") is a crucial period in the drug development process, as decisions to continue or halt development of a compound must be made with incomplete information. Incorrect decisions have large opportunity costs (the lost sales of a halted good drug or the unfunded alternative good projects with funds wasted on a bad drug). Relying solely on p-values from phase I-II studies for making drug development milestone decisions is an inefficient approach, as it ignores several important determinants of future success. This course will discuss, at length, the statistical tools that enable quantification of the uncertainty associated with the results coming from learning stage studies. These tools use the Bayesian approach to exploit the totality of accumulated data/knowledge in a formal way for internal decisionmaking in early drug development. Posterior and/or predictive probabilities computed in a Bayesian paradigm are easy to interpret and provide much more relevant information than p-values for decisionmaking. The discussion will be extended to evaluation of probability of a successful phase III trial through clinical trial simulations. Examples from the CNS, inflammation, and oncology therapeutic areas will be considered for evaluation of probability of success for drug candidates in meeting target product profile using a normal-dynamic linear model in a Bayesian framework. | ||
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JSM 2009
For information, contact jsm@amstat.org
or phone (888) 231-3473. If you have questions about the Continuing Education program,
please contact the Education Department. |