Statistics and Data Science Seminar
Qianshun Cheng / Tian Tian
UIC
A general and efficient algorithm for multiple objective optimal design / Estimation efficiency in continual reassessment method
Abstract: (Cheng) An experiment often has several competing objectives cannot be characterized by only one of the standard optimality criteria.
Multiple objective optimal design aims to optimize the target objective while guarantee that efficiency of the other objectives interested
are above acceptable levels. Such optimality problem is in general challenging and typically be solved through algorithm approach.
The existing approaches either have high computation cost or have low accuracy. In this talk, I will present a new algorithm which can
be used for general multiple objective optimal design problems regardless of model settings. Compared with the existing approach, the
new algorithm enjoys low computation cost and high accuracy.
(Tian) A widely used approach of designing the phase I clinical trial is continual reassessment method (CRM), which has been shown
through many simulations to be more effective than other traditional approaches. In this talk, I will show that the CRM algorithm is
indeed efficient from the perspective of optimal design theory. Specifically, simple power model and logistic model -- two popular
models, are considered. For simple power model, I'll show the efficiency of CRM depends on the target toxicity rate and CRM is highly
efficient in practice. A remarkable fact is that the optimal design selects the dose level such that the corresponding toxicity rate is
around 0.2, which is exactly the commonly used target toxicity rate in clinical trials. Moreover, by incorporating the idea of optimal
design into the study, the percentage of toxicity occurrence in the trial will drop by a great amount. As for logistic model, I'll show that
the CRM approach is indeed optimal, which will justify the efficiency of the algorithm in theory.
Wednesday February 11, 2015 at 4:00 PM in SEO 636