Tuesday, July 23, 2024

9:00 AM - 10:34 AM

PLENARY HALL (M1 building upper floor)

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Details

It is well known that there is typically high patient heterogeneity associated with most diseases and, thus, a treatment that works for one patient may not be effective for another. The modern precision medicine paradigm acknowledges this heterogeneity and aims to develop and deliver therapeutics that are tailored to the individual patient. This effort is expected to lead to improved health outcomes among patients overall, thereby improving both individual and public health. In the first talk, Menggang Yu will present a methodology for optimal individualized treatment rule learning based on a study sample which may differ from the target population of interest. In the second talk, Sukjin Han, will address the issue of optimal treatment allocation targeting distributional welfare as opposed to average welfare. The proposed work will address the issue of undesirable treatment allocations in populations with highly heterogeneous individuals (e.g., with outliers), which is common with the latter type of welfare. The session will conclude with Hyung Park, who will present a functional additive regression model that can capture the effect of the interaction between a categorical treatment variable and a potentially large number of pretreatment functional covariates on a response variable.

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Presentations

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Chair