Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes

Details Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes

2012-02-19

arxiv.org/abs/1202.4177v1

Statistics

Methodology

Scientific paper

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a key decision point and dictates the next treatment action among the options available as a function of accrued information on the patient. Using data from a clinical trial or observational study, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q-learning and advantage (A-)learning are two main approaches for this purpose. We provide a detailed account of Q- and A-learning and study systematically the performance of these methods. The methods are illustrated using data from a study of depression.

Affiliated with

Also associated with

No associations

Rating

Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-419637