Modern approaches for evaluating individual treatment effects from clinical and real-world data

Music City Center 

In this talk (based on our recent tutorial in Statistics in Medicine) I review recent advances in statistical methods for identification and evaluation of Heterogeneous Treatment Effects (HTE), including subgroup identification, estimation of conditional average treatment effects (CATE), and individualized treatment regimens (ITR) using data from randomized clinical trials and observational studies. Several classes of methods including indirect approaches based on modeling response surface as a function of treatment and covariates, and direct approaches targeting causal estimands of interest. Selected approaches will be evaluated using simulated data mimicking randomized clinical trials and observational studies with non-random treatment assignment.

Personalized medicine

Subgroup identification

individualized treatment regimen