Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes
Abstrak
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 decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.
Topik & Kata Kunci
Penulis (4)
P. Schulte
A. Tsiatis
Eric B. Laber
M. Davidian
Akses Cepat
- Tahun Terbit
- 2012
- Bahasa
- en
- Total Sitasi
- 239×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1214/13-STS450
- Akses
- Open Access ✓