Center for Statistical Sciences; Presents ``A Starr Lectureship Seminar''
Abstract:
It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial designs in which the criteria for patient enrollment may be changed, in a preplanned manner, based on interim analyses. Since such designs allow data-dependent changes to the population enrolled, care must be taken to ensure strong control of the family-wise Type I error rate.
Our main contribution is a general method for constructing randomized trial designs that allow changes to the population enrolled based on interim data using a pre-specified decision rule, make no parametric model assumptions, and guarantee the asymptotic, family-wise Type I error rate is strongly controlled at a specified level.
As a demonstration of our method, we prove new, sharp results for a simple, two stage enrichment design. We then compare this design to fixed designs, focusing on each design's ability to determine overall and subpopulation specific treatment effects.
We also construct confidence intervals with asymptotically correct coverage probability. This is joint work with Mark van der Laan.
Brown Applied Mathematics Pattern Theory and Vision Seminar
Abstract:
The current consensus version of the neural basis of vision -- or of cognition in general -- makes it difficult to model some of the central operations to be performed during perception and learning. I will offer the dynamic link architecture as an alternative physical basis for cognitive processes, will discuss it as a natural framework for vision and other cognitive processes, and will illustrate and support the argument with the help of a concrete model of invariant object recognition.
[pizza will be provided]
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