Interactive Q-learning for Probabilities and Quantiles

By Kristin A. Linn, Eric B. Laber, Leonard A. Stefanski. 2014

7 citations recorded by the source corpus.

A dynamic treatment regime is a sequence of decision rules in which each decision rule recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “The IQ-learning framework was then extended to optimize functionals of the outcome distribution other than the expected value [169], [170].”
Estimation of Optimal Dynamic Treatment Regimes (2014, methodology): “Other advantages of Q-learning are as follows: (1) it can be extended to accommodate discrete outcomes [45,50], censored outcomes [49], and competing outcomes [54,55]; (2) measures of goodness of fit and visual diagnostics can be used to assess the quality of the fitted regression models in each sta”