Bayesian likelihood-based regression for estimation of optimal dynamic treatment regimes
| العنوان: | Bayesian likelihood-based regression for estimation of optimal dynamic treatment regimes |
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| المؤلفون: | Weichang Yu, Howard D Bondell |
| المصدر: | Journal of the Royal Statistical Society Series B: Statistical Methodology. |
| بيانات النشر: | Oxford University Press (OUP), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Statistics and Probability, Statistics, Probability and Uncertainty |
| الوصف: | Clinicians often make sequences of treatment decisions that can be framed as dynamic treatment regimes. In this paper, we propose a Bayesian likelihood-based dynamic treatment regime model that incorporates regression specifications to yield interpretable relationships between covariates and stage-wise outcomes. We define a set of probabilistically-coherent properties for dynamic treatment regime processes and present the theoretical advantages that are consequential to these properties. We justify the likelihood-based approach by showing that it guarantees these probabilistically-coherent properties, whereas existing methods lead to process spaces that typically violate these properties and lead to modelling assumptions that are infeasible. Through a numerical study, we show that our proposed method can achieve superior performance over existing state-of-the-art methods. |
| تدمد: | 1467-9868 1369-7412 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::90ef4a2e96980986887f008f745f1272 https://doi.org/10.1093/jrsssb/qkad016 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........90ef4a2e96980986887f008f745f1272 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14679868 13697412 |
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