Comment on "Dynamic treatment regimes: technical challenges and applications"
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Inference for parameters associated with optimal dynamic treatment regimes is challenging as these estimators are nonregular when there are non-responders to treatments. In this discussion, we comment on three aspects of alleviating this nonregularity. We first discuss an alternative approach for smoothing the quality functions. We then discuss some further details on our existing work to identify non-responders through penalization. Third, we propose a clinically meaningful value assessment whose estimator does not suffer from nonregularity.
[1] Eric B. Laber,et al. Dynamic treatment regimes: technical challenges and applications. , 2014, Electronic journal of statistics.
[2] Marloes H. Maathuis,et al. From Probability to Statistics and Back: High-Dimensional Models and Processes: A Festschrift in Honor of Jon A. Wellner , 2013 .