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Statistics Colloquium
March 28 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower
Hyunkeun (Ryan) Cho
Department of Statistics
Western Michigan University
We develop new modeling and estimation for personalized treatment for individuals with high heterogeneity. Incorporating subject-specific information into treatment subgroup is critical since individuals could react to the same treatment quite differently. We propose to identify subgroups with longitudinal observations through random-effects estimation where the random effects are not necessarily normal distributed. The advantage of this approach is that we can quantify intrinsic associations between unobserved subject-specific effects and observed treatment outcomes, and therefore provide optimal treatment assignments for different individuals. In contrast, traditional mixed-effects models assuming normal distribution cannot effectively distinguish different patterns of treatment effects.
We develop asymptotic consistency theory for individual treatment effect estimation, and show that the new estimator is more efficient than the random effect estimator which ignores correlation information from longitudinal data. Simulation studies and a data example from an AIDS clinical trial group confirm that the proposed method is quite efficient in identifying an effective treatment strategy for subgroups in finite samples.
All statistics students are expected to attend.