SSRN Author: Archis GhateArchis Ghate SSRN Content
http://www.ssrn.com/author=1497543
http://www.ssrn.com/rss/en-usWed, 07 Oct 2015 02:33:49 GMTeditor@ssrn.com (Editor)Wed, 07 Oct 2015 02:33:49 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Robust Response-Guided DosingIn response-guided dosing (RGD), doses are adapted to the uncertain progression of each patient's disease condition. A stochastic dynamic program was recently developed for RGD. We study its robust counterpart, where the dose-response distribution belongs to an uncertainty set. For interval uncertainty, we prove that it is optimal to administer higher doses in worsening disease. When a certain scaling of a nominal distribution describes the interval, optimal doses also increase in the scaling parameter. Theory is illustrated via numerical results.
http://www.ssrn.com/abstract=2669694
http://www.ssrn.com/1434080.htmlTue, 06 Oct 2015 10:57:39 GMTNew: Optimal Bayesian Learning of Dose-Response Parameters from a CohortThere 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.
In this paper, we study the problem of finding optimal RGD policies while learning the ...
http://www.ssrn.com/abstract=2630392
http://www.ssrn.com/1412078.htmlWed, 15 Jul 2015 06:59:04 GMTNew: Multi-Class, Multi-Resource Advance Scheduling with No-Shows, Cancellations and OverbookingWe investigate a class of scheduling problems where dynamically and stochastically arriving requests for appointments are either rejected or booked for future slots. A customer may cancel an appointment. A customer who does not cancel may, with some probability, fail to show up. The planner may overbook appointments to mitigate the detrimental effects of cancellations and no-shows. A customer needs multiple renewable resources while in service. The system receives a reward for providing service to a customer; and incurs a cost of rejecting requests, a cost for appointment delays, and a cost of overtime. Customers are heterogeneous in their arrival patterns; costs and rewards; resource consumptions; and cancellation and no-show behaviors. Such advance scheduling problems arise in healthcare, revenue management, manufacturing, telecommunications, civilian and military logistics, and high performance computing.
We provide a Markov decision process (MDP) formulation of these problems. ...
http://www.ssrn.com/abstract=2550560
http://www.ssrn.com/1365791.htmlSat, 17 Jan 2015 07:13:54 GMTNew: Inverse Optimization in Countably Infinite Linear ProgramsGiven the objective coefficients and a feasible solution for a linear program, inverse optimization involves finding a new vector of objective coefficients that (i) is as close as possible to the original vector; and (ii) would make the given feasible solution optimal. This problem is well-studied for finite-dimensional linear programs. We develop a duality-based inverse optimization framework for countably infinite linear programs (CILPs) -- problems that include a countably infinite number of variables and constraints. Using the standard weighted absolute sum metric to quantify distance between cost vectors, we provide conditions under which constraints in the inverse optimization problem can be reformulated as a countably infinite set of linear constraints. We propose a convergent algorithm to solve the resulting infinite-dimensional mathematical program. This algorithm involves solving a sequence of finite-dimensional linear programs. We apply these results to inverse ...
http://www.ssrn.com/abstract=2529164
http://www.ssrn.com/1353393.htmlSat, 22 Nov 2014 16:18:04 GMTNew: Response-Guided Dosing for Rheumatoid ArthritisRheumatoid arthritis (RA) is an auto-immune disease with an unknown cause. Many patients receiving traditional methotrexate treatment continue to exhibit progressive joint damage and are then often treated with biologics. Biologic treatment is difficult owing to the uncertainty in dose-response, high cost, side effects, and intravenous administration. Recent clinical trials have therefore attempted response-guided dosing (RGD), where the hope is to adapt biologic doses over the treatment course based on each individual patient's observed evolution of the 28-joint disease activity score (DAS28). We provide a rigorous, stochastic dynamic programming (DP) framework to facilitate RGD.
We first present a concrete formulation where the DAS28 response is modeled using a stochastic Michaelis-Menten formula. The goal is to balance the DAS28 attained at the end of the course with the weighted total dose administered. We perform numerical experiments using data from the OPTION trial and ...
http://www.ssrn.com/abstract=2523767
http://www.ssrn.com/1350912.htmlThu, 13 Nov 2014 20:10:15 GMT