Quantile-optimal Treatment Regimes with Censored Data

Thursday, November 8, 2018 - 4:30pm - 5:00pm
Lind 305
Lan Wang (University of Minnesota, Twin Cities)
The problem of estimating an optimal treatment regime has received considerable attention recently. However, most of the earlier work in this area has focused on
estimating a mean-optimal treatment regime based on completely observed data. We
investigate a new quantile criterion for estimating an optimal treatment regime with rightcensored survival outcomes. When the outcome distribution is skewed or the censoring
is heavy, the quantile criterion is easy to interpret and provides an attractive measure of
treatment effect. In contrast, the mean criterion often can not be reliably estimated in
such settings. We propose a nonparametric approach to robustly estimating the
quantile-optimal treatment regime from a class of candidate treatment regimes without imposing
an outcome regression model. We derive a nonstandard converge rate and a non-normal
limiting distribution for the estimated parameters indexing the optimal treatment regime using advanced empirical processes theory. Such a theory has not been established in any
earlier work for survival data. We also extend the method to a two-stage dynamic setting.
We illustrate the practical utility of the proposed new method through Monte Carlo studies
and an application to a clinical trial data set. (Joint work with Yu Zhou and Rui Song)