C-learning: A new classification framework to estimate optimal dynamic treatment regimes
| Autor: | Baqun Zhang, Min Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Statistics and Probability Mathematical optimization Q-learning Decision tree Machine learning computer.software_genre 01 natural sciences Outcome (game theory) General Biochemistry Genetics and Molecular Biology 010104 statistics & probability 03 medical and health sciences Dynamic treatment regime Point (geometry) 0101 mathematics Flexibility (engineering) General Immunology and Microbiology business.industry Applied Mathematics General Medicine Decision rule Regression 030104 developmental biology Geography Artificial intelligence General Agricultural and Biological Sciences business computer |
| Zdroj: | Biometrics. 74:891-899 |
| ISSN: | 0006-341X |
| DOI: | 10.1111/biom.12836 |
| Popis: | A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. |
| Databáze: | OpenAIRE |
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