Cost-Effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Montecarlo Simulation

Colorectal cancer (CRC) is one of the deadliest types of cancer in the US due to its high incidence and mortality rates. Detection of CRC in the early stages through available screening tests increases the patient\u27s survival chances. In this study, we investigate the cost-effectiveness of a wide variety of multi-modal CRC screening policies. More specifically, we develop a Monte Carlo simulation framework to model the CRC natural history and preventive interventions. Age-specific and size-specific progression rates of adenomatous polyps are estimated using an innovative active learning method. Specifically, we develop a decision tree model to estimate size-specific and age-specific adenoma progression and regression rates. Compared to traditional methods, the proposed calibration process expedites the searching of the model parameter space significantly. CRC age-specific incidence rates and CRC stage distribution are the two output measures used in the calibration process. Seventy-eight CRC screening policies are applied to a cohort of U.S. male population using the simulation model and compared in terms of expected Quality Adjusted Life Years (QALY) and costs. Eleven policies are identified as efficient frontier policies. Among these 9 are identified as cost- effective at the willingness to pay (WTP) threshold of $50,000. Fecal Occult Blood Test (FOBT) biennially in conjunction with one time Colonoscopy at 60, FOBT biennially along with one time Colonoscopy at 50, Fecal Immunochemical Test (FIT) biennially in conjunction with two times Flexible Sigmoidoscopy (FS) at 60 and 65. FIT biennially with one time Colonoscopy at 65, Colonoscopy at 50, 60 and 70, FOBT biennially along with two times Colonoscopy at 55 and 65, FOBT annually with 2 times FS at 70 and 75, FOBT annually in conjunction with FS at 50 and 55, and FIT biennially along with FS every 5 years are the nine identified cost-effective policies

Stochastic Models for Improving Screening and Surveillance Decisions for Prostate Cancer Care

Recent advances in the development of new technologies for the early detection and treatment of cancer have the potential to improve patient survival and lower the cost of treatment by catching cancer at an early stage. However, there is little research investigating the health and economic implications of these new technologies. For example, magnetic resonance imaging (MRI) and new biomarker tests have been proposed as potential minimally invasive ways to achieve early detection of prostate cancer. These new technologies vary in their sensitivity and specificity leading to both false-positive and false-negative results that can have serious health implications for patients. Moreover, due to the high cost and imperfect nature of these new tests, whether and when to use these tests is unclear. We present stochastic models for prostate cancer disease onset and progression that incorporates partial observability of a patient's prostate cancer health status. We used statistical learning algorithms and clinical datasets combined with expert clinical knowledge of urologists at the University of Michigan to estimate and validate the models. The models can simulate progression through prostate cancer states to mortality from prostate cancer or other causes for a population of patients. New technologies, such as MRI and biomarker tests, are incorporated into the model using a probabilistic representation of test outcomes to represent the information these tests provide about the true health status of the patient. Since these technologies can be used in varying ways, the choice of tests and optimal times to initiate tests are treated as decision variables in the model. We calibrated and validated our models using several data sources and subsequently used our models to design optimal testing strategies that trade-off the harms and benefits of using these new technologies. Our results show that these new technologies can lead to significantly improved health outcomes and they are cost-effective relative to established norms for societal willingness-to-pay. We have also used these models to provide important insights about the optimal timing of prostate biopsies for men with low-risk prostate cancer undergoing active surveillance. By using new technologies to better select men for biopsy and by improving active surveillance strategies, physicians can reduce the harms of prostate cancer screening (e.g., unnecessary biopsies and overtreatment of low-risk disease) while continuing to reduce prostate cancer deaths through screening and early detection. The methodological approaches we present in this thesis could be applied to many other chronic diseases, including bladder, breast, and colorectal cancer.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136969/1/clbarnet_1.pd

Molecular testing for Lynch syndrome in people with colorectal cancer: systematic reviews and economic evaluation

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Inherited mutations in deoxyribonucleic acid (DNA) mismatch repair (MMR) genes lead to an increased risk of colorectal cancer (CRC), gynaecological cancers and other cancers, known as Lynch syndrome (LS). Risk-reducing interventions can be offered to individuals with known LS-causing mutations. The mutations can be identified by comprehensive testing of the MMR genes, but this would be prohibitively expensive in the general population. Tumour-based tests - microsatellite instability (MSI) and MMR immunohistochemistry (IHC) - are used in CRC patients to identify individuals at high risk of LS for genetic testing. MLH1 (MutL homologue 1) promoter methylation and BRAF V600E testing can be conducted on tumour material to rule out certain sporadic cancers. OBJECTIVES: To investigate whether testing for LS in CRC patients using MSI or IHC (with or without MLH1 promoter methylation testing and BRAF V600E testing) is clinically effective (in terms of identifying Lynch syndrome and improving outcomes for patients) and represents a cost-effective use of NHS resources. REVIEW METHODS: Systematic reviews were conducted of the published literature on diagnostic test accuracy studies of MSI and/or IHC testing for LS, end-to-end studies of screening for LS in CRC patients and economic evaluations of screening for LS in CRC patients. A model-based economic evaluation was conducted to extrapolate long-term outcomes from the results of the diagnostic test accuracy review. The model was extended from a model previously developed by the authors. RESULTS: Ten studies were identified that evaluated the diagnostic test accuracy of MSI and/or IHC testing for identifying LS in CRC patients. For MSI testing, sensitivity ranged from 66.7% to 100.0% and specificity ranged from 61.1% to 92.5%. For IHC, sensitivity ranged from 80.8% to 100.0% and specificity ranged from 80.5% to 91.9%. When tumours showing low levels of MSI were treated as a positive result, the sensitivity of MSI testing increased but specificity fell. No end-to-end studies of screening for LS in CRC patients were identified. Nine economic evaluations of screening for LS in CRC were identified. None of the included studies fully matched the decision problem and hence a new economic evaluation was required. The base-case results in the economic evaluation suggest that screening for LS in CRC patients using IHC, BRAF V600E and MLH1 promoter methylation testing would be cost-effective at a threshold of £20,000 per quality-adjusted life-year (QALY). The incremental cost-effectiveness ratio for this strategy was £11,008 per QALY compared with no screening. Screening without tumour tests is not predicted to be cost-effective. LIMITATIONS: Most of the diagnostic test accuracy studies identified were rated as having a risk of bias or were conducted in unrepresentative samples. There was no direct evidence that screening improves long-term outcomes. No probabilistic sensitivity analysis was conducted. CONCLUSIONS: Systematic review evidence suggests that MSI- and IHC-based testing can be used to identify LS in CRC patients, although there was heterogeneity in the methods used in the studies identified and the results of the studies. There was no high-quality empirical evidence that screening improves long-term outcomes and so an evidence linkage approach using modelling was necessary. Key determinants of whether or not screening is cost-effective are the accuracy of tumour-based tests, CRC risk without surveillance, the number of relatives identified for cascade testing, colonoscopic surveillance effectiveness and the acceptance of genetic testing. Future work should investigate screening for more causes of hereditary CRC and screening for LS in endometrial cancer patients. STUDY REGISTRATION: This study is registered as PROSPERO CRD42016033879. FUNDING: The National Institute for Health Research Health Technology Assessment programme.Funding for this study was provided by the Health Technology Assessment programme of the National Institute for Health Researc

Personalized Decision Modeling for Intervention and Prevention of Cancers

Personalized medicine has been utilized in all stages of cancer care in recent years, including the prevention, diagnosis, treatment and follow-up. Since prevention and early intervention are particularly crucial in reducing cancer mortalities, personalizing the corresponding strategies and decisions so as to provide the most appropriate or optimal medical services for different patients can greatly improve the current cancer control practices. This dissertation research performs an in-depth exploration of personalized decision modeling of cancer intervention and prevention problems. We investigate the patient-specific screening and vaccination strategies for breast cancer and the cancers related to human papillomavirus (HPV), representatively. Three popular healthcare analytics techniques, Markov models, regression-based predictive models, and discrete-event simulation, are developed in the context of personalized cancer medicine. We discuss multiple possibilities of incorporating patient-specific risk into personalized cancer prevention strategies and showcase three practical examples. The first study builds a Markov decision process model to optimize biopsy referral decisions for women who receives abnormal breast cancer screening results. The second study directly optimizes the annual breast cancer screening using a regression-based adaptive decision model. The study also proposes a novel model selection method for logistic regression with a large number of candidate variables. The third study addresses the personalized HPV vaccination strategies and develops a hybrid model combining discrete-event simulation with regression-based risk estimation. Our findings suggest that personalized screening and vaccination benefit patients by maximizing life expectancies and minimizing the possibilities of dying from cancer. Preventive screening and vaccination programs for other cancers or diseases, which have clearly identified risk factors and measurable risk, may all benefit from patient-specific policies

Management of a Chronically Ill Population: An Operations Approach to Liver Cancer Screening.

We study how to perform medical surveillance of a population living with a chronic disease from an operations perspective. Our approach to the screening problem is the first to combine aspects of patient specific risk factors, heterogenous disease progression, as well limited screening resources shared by the population. Using clinical data from liver cancer as a motivating example, we (1) provide a new characterization of individualized risk for liver cancer through a nested case-control match study, then (2) demonstrate the utility of that individual biological information in screening decisions through the design and testing of reinforcement learning techniques, and then (3) model the problem as a family of restless bandits to gain structural insights into the problem, as well as derive an optimal policy to screen patients. Ultimately, we provide novel methods of screening a chronically ill population which are superior to current practice by adopting principles from a broad spectrum of operations methods.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133270/1/elliotdl_1.pd

Mathematical modeling of Lynch syndrome carcinogenesis

Cancer is one of the leading causes of disease-related death worldwide. In recent years, large amounts of data on cancer genetics and molecular characteristics have become available and accumulated with increasing speed. However, the current understanding of cancer as a disease is still limited by the lack of suitable models that allow interpreting these data in proper ways. Thus, the highly interdisciplinary research field of mathematical oncology has evolved to use mathematics, modeling, and simulations to study cancer with the overall goal to improve clinical patient care. This dissertation aims at developing mathematical models and tools for different spatial scales of cancer development at the example of colorectal cancer in Lynch syndrome, the most common inherited colorectal cancer predisposition syndrome. We derive model-driven approaches for carcinogenesis at the DNA, cell, and crypt level, as well as data-driven methods for cancer-immune interactions at the DNA level and for the evaluation of diagnostic procedures at the Lynch syndrome population level. The developed models present an important step toward an improved understanding of hereditary cancer as a disease aiming at rapid implementation into clinical management guidelines and into the development of novel, innovative approaches for prevention and treatment

Loan modifications and risk of default: a Markov chains approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementWith the housing crisis, credit risk analysis has had an exponentially increasing importance, since it is a key tool for banks’ credit risk management, as well as being of great relevance for rigorous regulation. Credit scoring models that rely on logistic regression have been the most widely applied to evaluate credit risk, more specifically to analyze the probability of default of a borrower when a credit contract initiates. However, these methods have some limitations, such as the inability to model the entire probabilistic structure of a process, namely, the life of a mortgage, since they essentially focus on binary outcomes. Thus, there is a weakness regarding the analysis and characterization of the behavior of borrowers over time and, consequently, a disregard of the multiple loan outcomes and the various transitions a borrower may face. Therefore, it hampers the understanding of the recurrence of risk events. A discrete-time Markov chain model is applied in order to overcome these limitations. Several states and transitions are considered with the purpose of perceiving a borrower’s behavior and estimating his default risk before and after some modifications are made, along with the determinants of post-modification mortgage outcomes. Mortgages loans are considered in order to take a reasonable timeline towards a proper assessment of different loan performances. In addition to analyzing the impact of modifications, this work aims to identify and evaluate the main risk factors among borrowers that justify transitions to default states and different loan outcomes

Data-Driven Decision-Making for Medications Management Modalities

abstract: One of the critical issues in the U.S. healthcare sector is attributed to medications management. Mismanagement of medications can not only bring more unfavorable medical outcomes for patients, but also imposes avoidable medical expenditures, which can be partially accounted for the enormous $750 billion that the American healthcare system wastes annually. The lack of efficiency in medical outcomes can be due to several reasons. One of them is the problem of drug intensification: a problem associated with more aggressive management of medications and its negative consequences for patients. To address this and many other challenges in regard to medications mismanagement, I take advantage of data-driven methodologies where a decision-making framework for identifying optimal medications management strategies will be established based on real-world data. This data-driven approach has the advantage of supporting decision-making processes by data analytics, and hence, the decision made can be validated by verifiable data. Thus, compared to merely theoretical methods, my methodology will be more applicable to patients as the ultimate beneficiaries of the healthcare system. Based on this premise, in this dissertation I attempt to analyze and advance three streams of research that are influenced by issues involving the management of medications/treatments for different medical contexts. In particular, I will discuss (1) management of medications/treatment modalities for new-onset of diabetes after solid organ transplantations and (2) epidemic of opioid prescription and abuse.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201