|Title||Entropy Learning for Dynamic Treatment Regimes.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Jiang, Binyan, Rui Song, Jialiang Li, and Donglin Zeng|
Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not been well established in machine learning based approaches. In this paper, we propose a entropy learning approach to estimate the optimal individualized treatment rules (ITRs). We obtain the asymptotic distributions for the estimated rules so further provide valid inference. The proposed approach is demonstrated to perform well in finite sample through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings that are otherwise not revealed with existing approaches.
|Alternate Journal||Stat Sin|
|Original Publication||Entropy learning for dynamic treatment regimes.|
|PubMed Central ID||PMC6750237|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM124104 / GM / NIGMS NIH HHS / United States
Entropy Learning for Dynamic Treatment Regimes.