The Optimal Timing of Living-Donor Liver Transplantation

Alagöz, Oğuzhan; Maillart, Lisa M.; Schaefer, Andrew J.; Roberts, Mark S.

doi:10.1287/mnsc.1040.0287

Cited by 172 publications

(157 citation statements)

References 31 publications

(26 reference statements)

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“…For instance, Alagoz et al (2004) used an MDP model to study the structure of optimal policies for the timing of living-donor liver transplantation; Denton et al (2009) used an MDP model to study optimal policies for initiating cholesterol lowering medication for patients with diabetes; and Shechter et al (2005) used an MDP model to study the optimal time to initiate HIV therapy. Chhatwal et al (2010) used a MDP to study the optimal policy for breast cancer biopsy based on mammography observations.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Prostate Biopsy Referral Decisions

Zhang

Denton

Balasubramanian

et al. 2012

M&SOM

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Prostate cancer is the most common solid tumor in American men and is screened for using prostate-specific antigen (PSA) tests. We report on a non-stationary partially observable Markov decision process (POMDP) for prostate biopsy referral decisions. The core states are the patients' prostate cancer related health states, and PSA test results are the observations. Transition probabilities and rewards are inferred from the Mayo Clinic Radical Prostatectomy Registry (MCRPR) and the medical literature. The objective of our model is to maximize expected quality-adjusted life years (QALYs). We solve the POMDP model to obtain an age and belief (probability of having prostate cancer) dependent optimal biopsy referral policy. We also prove a number of structural properties including the existence of a control-limit type policy for the biopsy referral decision. Our empirical results demonstrate a non-decreasing belief threshold in age, and we provide sufficient conditions under which PSA screening should be discontinued for older patients. Finally, the benefits of screening under the optimal biopsy referral policy are estimated, and sensitivity analysis is used to prioritize the model parameters that would benefit from additional data collection.

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Section: Introductionmentioning

confidence: 99%

Optimization of Prostate Biopsy Referral Decisions

Zhang

Denton

Balasubramanian

et al. 2012

M&SOM

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“…In contrast, we define an expected benefit loss function and guarantee the optimality of control-limit policies given the monotonicity of this function. Therefore, our conditions do not require any special structure for the optimal value function of the model, and in this sense are less-restrictive than those of Alagoz et al (2004).…”

Section: Structural Propertiesmentioning

confidence: 98%

“…While MDP models have been applied in other clinical contexts (see Alagoz et al (2010) for a review of discrete-time MDP models in health care), we provide several extensions. Specifically, Alagoz et al (2004) provides sufficient conditions to prove an optimal control-limit policy within the context of living-donor liver transplantation. The authors consider a stationary infinite-horizon MDP.…”

Section: Structural Propertiesmentioning

confidence: 99%

“…We begin our analyses by defining the notion of an "increasing failure rate" (IFR) transition probability matrix, which has been shown to be useful in proving the structural properties of MDPs in maintenance and reliability theory (Valdez-Flores and Feldman 1989) and appears to match clinical data closely in varying contexts (Alagoz et al, 2004, Kreke et al, 2008.…”

Section: Structural Propertiesmentioning

confidence: 99%

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The structure of optimal statin initiation policies for patients with Type 2 diabetes

Kurt¹,

Denton²,

Schaefer³

et al. 2011

IIE Transactions on Healthcare Systems Engineering

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Lipid abnormalities increase the risk of coronary heart disease (CHD) and stroke in patients with Type 2 diabetes. Statins can be used to treat these abnormalities, but may have adverse side effects. In this article, we consider the optimal timing of statin initiation for patients with Type 2 diabetes. We formulate an infinite-horizon Markov decision process to maximize the patient's quality-adjusted life years (QALYs) prior to the occurrence of the first CHD or stroke event. We describe the state of the process by the patient's lipid ratio levels, and derive structural properties of the resulting optimal stopping time model, including sufficient conditions for the optimality of control-limit policies with respect to patient's lipid ratio levels and age. We use a large clinical data set to parameterize our model and compute optimal treatment policies to demonstrate the clinical implications of our results. We illustrate the importance of individualized treatment factors by estimating the patients' QALY gains using our customized policies rather than current U.S. guidelines.

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“…In the medical literature, Markov models have explored very diverse problems such as timing of liver transplant [8], HIV therapy [9], breast cancer [10], Hepatitis C [11], statin therapy [12] or hospital discharge management [5,13]. Markov models can be used to describe various health states in a population of interest, and to detect the effects of various policies or therapeutic choices.…”

Section: Medical Applications Of Markov Modelsmentioning

confidence: 99%

Markov Models and Cost Effectiveness Analysis: Applications in Medical Research

Komorowski

Raffa

2016

Secondary Analysis of Electronic Health Records

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Learning ObjectivesUnderstand how Markov models can be used to analyze medical decisions and perform cost-effectiveness analysis.This case study introduces concepts that should improve understanding of the following:1. Markov models and their use in medical research. 2. Basics of health economics. 3. Replicating the results of a large prospective randomized controlled trial using a Markov Chain and Monte Carlo simulations, and 4. Relating quality-adjusted life years (QALYs) and cost of interventions to each state of a Markov Chain, in order to conduct a simple cost-effectiveness analysis. IntroductionMarkov models were initially theroreticized at the beginning of the 20th century by Russian mathematician Andrey Markov [1]. They are stochastic processes that undergo transitions from one state to another. Over the years, they have found countless applications, especially for modeling processes and informing decision making, in the fields of physics, queuing theory, finance, social sciences, statistics and of course medicine. Markov models are useful to model environments and problems involving sequential, stochastic decisions over time. Representing such environments with decision trees would be confusing or intractable, if at all possible, and would require major simplifying assumptions [2]. Markov models can be examined by an array of tools including linear algebra (brute force), cohort simulations, Monte Carlo simulations and, for Markov Decision Processes, dynamic programming and reinforcement learning [3,4].

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The Optimal Timing of Living-Donor Liver Transplantation

Cited by 172 publications

References 31 publications

Optimization of Prostate Biopsy Referral Decisions

Optimization of Prostate Biopsy Referral Decisions

The structure of optimal statin initiation policies for patients with Type 2 diabetes

Markov Models and Cost Effectiveness Analysis: Applications in Medical Research

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