کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6895399 1445942 2018 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian learning of dose-response parameters from a cohort under response-guided dosing
ترجمه فارسی عنوان
یادگیری بیزی از پارامترهای دوز پاسخ از یک گروه در دوز واکنش هدایت
کلمات کلیدی
یا در پزشکی، برنامه نویسی دینامیک، فرایندهای تصمیم گیری مارکوف، بهینه سازی محدب، برنامه ریزی پزشکی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی
There has been a surge of clinical interest in the idea of response-guided dosing (RGD). The goal in RGD is to tailor drug-doses to the stochastic evolution of each individual patient's disease condition over the treatment course. The hope is that this form of individualized therapy will deliver the right dose to the right patient at the right time. Several expert panels have observed that despite the excitement surrounding RGD, quantitative, data-driven decision-making approaches that learn patients' dose-response and incorporate this information into adaptive dosing strategies are lagging behind. This situation is particularly exacerbated in clinical trials. For instance, fixed design clinical studies for estimating the key parameter of a dose-response function might not treat trial patients optimally. Similarly, the dosing strategies employed in clinical trials for RGD often appear ad-hoc.We study the problem of finding optimal RGD policies while learning the distribution of a dose-response parameter from a cohort of patients. We provide a Bayesian stochastic dynamic programming (DP) formulation of this problem. Exact solution of Bellman's equations for this problem is computationally intractable. We therefore present two approximate control schemes and mathematically analyze the monotonicity, stationarity, and separability structures of the resulting dosing strategies. These structures are then exploited in efficient, approximate solution of our problem. Computer simulations using the Michaelis-Menten dose-response function are included as an example wherein we study the effect of cohort size and of prior misspecification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 265, Issue 1, 16 February 2018, Pages 328-343
نویسندگان
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