Ashkan Ertefaie

Selective inference for dynamic treatment regimes via the LASSO

Constructing an optimal dynamic treatment regime become complex when there are large number of prognostic factors, such as patient’s genetic information, demographic characteristics, medical history over time. Existing methods only focus on selecting the important variables for the decision-making process and fall short in providing inference for the selected model. We fill this gap by leveraging the conditional selective inference methodology. We show that the proposed method is asymptotically valid given certain rate assumptions in semiparametric regression.

Ashkan Ertefaie is an Assistant Professor in the Dept of Biostatistics and Computational Biology at the the University of Rochester. He is a McGill alumnus with a PhD degree in Statistics, under co-supervision of Professors David Stephens and Masoud Asgharian. His research interest lies in causal inference, dynamic treatment regimes, sequential multiple assignment randomized trials, comparative effectiveness studies using electronic health records, instrumental variable analyses, high-dimensional data analysis, post selection inference, and survival analysis.