DISSERTATION PROPOSAL DEFENSE ANNOUNCEMENT
FOR THE DEGREE OF PHD IN BUSINESS
PhD Student: Ying Wang
Title: Use Data and Evidence to Improve Quality of Life for Patients with Alzheimer’s Disease
Date & Time: Thursday, Nov 11th, 2021, 2:00pm – 4:00pm
Location: Smith 122 or via remote connection
Remote Connection: https://bentley.zoom.us/j/96465389915
Remote Connection Instructions: https://goo.gl/6RxFTd
Dissertation Committee Chair: Dominique Haughton, Professor of Mathematical Sciences, Bentley University
Committee Members: Mingfei Li, Professor of Mathematical Sciences, Bentley University; Jennifer Priestley, Professor of Statistics and Data Science, Kennesaw State University
ABSTRACT
As an irreversible, progressive brain disorder, Alzheimer’s disease (AD) imposes a severe burden upon patients and their caregivers, as well as the healthcare system. Of the ten leading causes of death in the United States, Alzheimer’s disease is the only one without a pharmacological intervention that has been proven to cure or delay the onset of the disease. Aging is the primary risk factor contributing to Alzheimer's disease in the elderly. With an aging population that continues to grow, the challenges for the healthcare system surrounding AD become more and more serious. My dissertation aims to contribute to a better understanding of this rising problem from big data analytics point of view. A large-scale national clinical and administrative data warehouse in the Veteran Affairs healthcare system will be used for the following investigation.
The first paper investigates the association of several commonly used medications of hypertension and hypercholesterolemia with the prolonged pre-symptomatic time of the patients. The investigated medications have been approved by FDA for other indications which have been well-understood as a risk factor for AD. The generic version of these medications is covered by most Medicare and insurance plans. Any direct or indirect effect of these medications on prolonging the pre-symptomatic time of the patients would have a huge economic impact on the patients, caregivers, and the healthcare system. The beneficial effects of the use of the statin medications (Atorvastatin versus Simvastatin) and the use of the angiotensin-converting enzyme inhibitor (ACEI) Lisinopril were found to delay the occurrence of clinical manifestations of Alzheimer’s disease (AD) using Cox regression with propensity score weights.
The second paper studied the AD disease progression, therapeutic interventions, and health resource utilization at each stage of AD with the data from the Veteran Affairs healthcare system. Starting from the early phase mild cognitive impairment (MCI) to AD, then to death, the transition rates and probabilities between different disease phases were presented using the Markov multi-state modeling. Risk factors that facilitate the progression from MCI to AD were identified using Cox regression with propensity score weights. The understanding of the disease progression of AD will contribute to the estimate of the costs related to AD, and understand the cost-effectiveness of AD-related treatments and services.
The third paper proposes to investigate the robustness of multi-state survival models when dealing with noisy data. Diagnosis is a highly complex task. Missed, delayed, or wrong diagnoses are inevitable in the healthcare system, especially in the primary care setting where a high volume of diagnostic decisions was made in a complex and uncertain environment. Diseases like Alzheimer's appear to have higher diagnostic errors due to the complication of the disease and the limited use of advanced measurements in primary care practices. As such, the understanding of the performance of an algorithm under a high noisy data environment is crucial. However, the related discussion in the literature is limited. In this study, I planned to compare several multi-state survival models (such as Markov multi-state, multi-state random survival) with real and simulated data. This work could contribute to evaluating the sensitivity of the prediction results from multi-stage models for the disease progression analysis.
Thursday, November 11, 2021 at 2:00pm to 4:00pm
Smith Technology Center, Classroom 122, Smith 122
Smith Technology Center 122, Bentley University, 175 Forest Street, Waltham MA 02452
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