Assessing Dynamic Breast Cancer Screening Policies

Maillart, Lisa M.; Ivy, Julie S.; Ransom, Scott B.; Diehl, Kathleen M.

doi:10.1287/opre.1080.0614

Cited by 121 publications

(62 citation statements)

References 28 publications

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“…From the perspective of our model, this can be interpreted to mean that once patients are biopsied they leave the system. This assumption has also been made in previous cancer screening studies (Maillart et al 2008, Chhatwal et al 2010.…”

Section: Structural Propertiesmentioning

confidence: 60%

“…Hauskrecht and Fraser (2000) applied a POMDP formulation to the problem of treating patients with ischemic heart disease. Maillart et al (2008) used a partially observable Markov process to study breast cancer screening policies using mammography; they evaluated age-dependent screening policies and studied the tradeoff between lifetime mortality risk of breast cancer and the expected number of mammograms. Ivy (2008) further studied a POMDP model of breast cancer screening; they considered the patient and third-party payer perspective by computing cost optimal screening policies subject to a patient-based utility constraint at each decision epoch.…”

Section: Introductionmentioning

confidence: 99%

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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: Structural Propertiesmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Optimization of Prostate Biopsy Referral Decisions

Zhang

Denton

Balasubramanian

et al. 2012

M&SOM

View full text Add to dashboard Cite

show abstract

“…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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“…Faissol et al [18] develop a Markov decision process model to identify dynamic, risk-based screening policies for Hepatitis C. They take high-risk subgroups into account in order to establish who should be screened. Maillart et al [19] introduce a partially observable Markov chain model to evaluate dynamic breast cancer screening policies in order to identify efficient, robust policies. Ozekici and Pliska [20] examine the impact of Type I and Type II errors on breast cancer screening policies.…”

Section: Literature Reviewmentioning

confidence: 99%

Using simulation-optimization to construct screening strategies for cervical cancer

McLay

Foufoulides

Merrick

2010

Health Care Manag Sci

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Cervical cancer is the second most common cancer in women worldwide. Cervical screening is critical for preventing this type of cancer. Traditionally, screening strategies are evaluated from an economic point of view through cost-effectiveness analysis. However, cost-effectiveness analysis is typically performed on a limited number of de facto or predetermined screening policies. We develop a simulation-optimization model to determine the ages at which screening should be performed, resulting in dynamic, age-based screening policies. We consider three performance measures: cervical cancer incidence, the number of cervical cancer deaths, and the number of life years lost due to cervical cancer death. Using each performance measure, we compare our optimal, dynamic screening strategies to standard policies considered in the health screening literature that are static and predetermined. We also evaluate the anticipated impact of vaccinations for preventing cervical cancer. The strategies that are developed are compared to those used in practice or considered in the literature. The Centers for Disease Control and Prevention recommends one screening every 3 years, resulting in 14 scheduled lifetime screenings. Our dynamic screening strategies provide approximately the same health benefits as this but with four to six fewer scheduled screenings, depending on the performance measure considered. Our dynamic strategies also provide approximately the same health benefits as screening every 2 years, but with six to nine fewer scheduled screenings. The results suggest that dynamic, age-based cervical cancer screening policies offer substantial economic savings in order to offer the same health benefits as equally spaced screening strategies.

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Assessing Dynamic Breast Cancer Screening Policies

Cited by 121 publications

References 28 publications

Optimization of Prostate Biopsy Referral Decisions

Optimization of Prostate Biopsy Referral Decisions

Markov Models and Cost Effectiveness Analysis: Applications in Medical Research

Using simulation-optimization to construct screening strategies for cervical cancer

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