Given the emerging importance of personalized medicine (PM) in primary care, now should be the ideal time for engaging with health care professionals (HCPs), both physicians and nurses, about integrating PM into practice. The question then becomes: what is the most effective way to engage with HCPs about emerging technologies that are not in routine clinical use and which are unfamiliar to many? The overall aim of this pilot study was to develop and compare two professional engagement (PE) approaches for engaging with HCPs about PM to inform their development and design of a future formal evaluation. The first PE intervention was a structured in-person focus group and the second was an online version, also incorporating an educational component, but without group interaction. The pilot study showed that while participants evaluated both interventions positively, the in-person workshop consistently scored higher; however, recruitment challenges were a major obstacle for this approach.

There has been increasing development in personalized interventions that are tailored to uniquely evolving health status of each patient over time. In this dissertation, we investigate two problems: (1) the construction of individualized mobile health (mHealth) application recommender system; and (2) the estimation of optimal dynamic treatment regimes (DTRs) from a multi-stage clinical trial study. The dissertation is organized as follows. In Chapter 1, we provide a brief background on personalized medicine and two motivating examples which illustrate the needs and benefits of individualized treatment policies. We then introduce reinforcement learning and various methods to obtain the optimal DTRs as well as Q-learning procedure which is a popular method in the DTR literature. In Chapter 2, we propose a partial regularization via orthogonality using the adaptive Lasso (PRO-aLasso) to estimate the optimal policy which maximizes the expected utility in the mHealth setting. We also derive the convergence rate of the expected outcome of the estimated policy to that of the true optimal policy. The PRO-aLasso estimators are shown to enjoy the same oracle properties as the adaptive Lasso. Simulations and real data application demonstrate that the PRO-aLasso yields simple, more stable policies with better results as compared to the adaptive Lasso and other competing methods. In Chapter 3, we propose a penalized A-learning with a Lasso-type penalty for the construction of optimal DTR and derive generalization error bounds of the estimated DTR. We first examine the relationship between value and the Q-functions, and then we provide a finite sample upper bound on the difference in values between the optimal DTR and the estimated DTR. In practice, we implement a multi-stage PRO-aLasso algorithm to obtain the optimal DTR. Simulation results show advantages of the proposed methods over some existing alternatives. The proposed approach is also demonstrated with the data from a depression clinical trial study. In Chapter 4, we present future work and concluding remarks.

Though racial classifications may serve as a mechanism for identifying and correcting disparities among various groups, using such classifications in a clinical setting to detect and treat patient needs can be problematic. This report explores how medical professionals and researchers use race in health care for purposes of data collection, risk assessment, and diagnosis and treatment options. Using mixed race individuals as an example, it then discusses some of the problems associated with using race to group individuals, assess risk, and inform patient care. Finally, it discusses how certain components of personalized medicine, such as genetic testing, Electronic Health Records, and Rapid Learning Systems could help address some of the concerns that arise from the application of race in a health care setting. / text

There have been many scientific developments in the last century including the atomic bomb and DNA sequencing. Moreover, when human genome was sequenced in the early 2000s, it opened a new avenue to study disease and human development. Genetic tests have become an integral part for cancer diagnosis. Still, cancer therapy is decided based on the tumor genotype, the very definition of pharmacogenetic testing. More specifically, pharmacogenetics or pharmacogenomics is defined as variations in genes that can affect drug response. There has been great deal of research into pharmacogenetics and its potential fields for application. One such field is cardiology and cardiovascular disease. There are some promising researches that indicate genetic influence over drug response, such as the role of CYP2C19 over metabolism of a drug used for treating acute coronary disease and other cardiovascular issues. This is a great tool in the transition toward personalized medicine; however there are some logistical and social concerns over genetic tests; test administration, result accuracy and validity, data storage and security. Also, many patients were concerned with confidentiality, payment method and timely intervention. Also, implementation plans should include all areas, not just cities. Although there is potential for pharmacogenetic testing, many challenges have to be considered and addressed to ensure public confidence and proper use of the technique. Pharmacogenetics is a step towards individualized or personalized medicine; in-depth research prior to implementation will help tackle any challenges that may arise.

Cancer therapy has historically been determined by a tumor’s tissue of origin. Now, thanks to advances in genomics technology, scientists are looking further into one’s cancer; into the very genome that drives the tumor growth. The growth of genomics in cancer research has been astronomical. In a little over ten years since the completion of the Human Genome Project, genomic profiling technologies have developed into an incredibly powerful, relatively cheap, and immensely underutilized tool for oncologists. In the midst of the advances in cancer profiling, there has been reluctance from oncologists to incorporate genomic profiling into their treatment decisions. Saddled by outdated clinical trial designs, and cancer drug regulation programs, the true measure of the clinical utility of genomic profiling has yet to be seen. Cancer scientists will continue to profile cancers at a pace well beyond the limits of the field of oncology. Without coordinated efforts to update the oncology healthcare system, compendia of data will continue to be generated with limited ability to translate the information into personalized medicines. There are significant barriers to overcome before genomic data can universally be incorporated into the daily practice of cancer medicine. In the meantime, resources are available for physicians to help begin the process of integrating a more personalized approach to cancer therapy. Third-party bioinformatics companies are in the best position to be the agents of this change. As cancer research continues to adopt a genomic approach, it is paramount that, for the sake of millions of cancer patients, the healthcare system adapts in a way to best utilize this new information.

The research of my dissertation studies the methods of designing and analyzing sequential multiple assignment randomized trial (SMART) for comparing multiple adaptive interventions. As a SMART typically consists of numerous adaptive interventions, inferential procedures based on pairwise comparisons of all interventions may suffer substantial loss in power after accounting for multiplicity. I address this problem using two approaches. First, I propose a likelihood-based Wald test, study the asymptotic distribution of its test statistics, and apply it as a gate-keeping test for making an adaptive intervention selection. Second, I consider a multiple comparison with the best approach by constructing simultaneous confidence intervals that compare the interventions of interest with the truly best intervention, which is assumed to be unknown in inference; an adaptive intervention with the proposed interval excluding zero will be declared as inferior to the truly best with a pre-specified confidence level. Simulation studies show that both methods outperform the corresponding multiple comparison procedures based on Bonferroni's correction in terms of the power of test and the average width of confidence intervals for estimation. Simulations also suggest desirable properties of the proposed methods. I apply these methods to analyze two real data sets. As part of the dissertation, I also develop a user-friendly R software package that covers many statistical work related to SMART, including study design, data analysis and visualization. Both proposed methods can be implemented by using this R package. In the end of the dissertation, I show an application of designing a SMART to compare multiple patient care strategies for depression management based on one of the proposed methods.

Conclusion: The majority of independent community pharmacists are interested in incorporating personalized medicine services into their practices, but they require further education before this is possible. Future initiatives should focus on the development of comprehensive education programs to further train pharmacists for provision of these services.Objectives: To evaluate the perceptions of independent community pharmacists within a regional independent community pharmacy cooperative on implementing personalized medicine services at their pharmacies and to gauge the pharmacists' self-reported knowledge of pharmacogenomic principles.Design: Descriptive, exploratory, nonexperimental study.Setting: American Pharmacy Services Corporation (APSC), 2011-12.Participants: Pharmacists (n = 101) affiliated with the independent pharmacies of APSC.Intervention: Single-mode surveyMain outcome measures: Independent community pharmacists' interest in implementing personalized medicine services, perceived readiness to provide such services, and perceived barriers to implementation.Results: 101 completed surveys were returned for data analysis. The majority of pharmacists surveyed (75%) expressed interest in offering personalized medicine services. When asked to describe their knowledge of pharmacogenomics and readiness to implement such services, more than 50% said they were not knowledgeable on the subject and would not currently be comfortable making drug therapy recommendations to physicians or confident counseling patients based on results of genetic screenings without further training and education. Respondents identified cost of providing the service, reimbursement issues, current knowledge of pharmacogenomics, and time to devote to the program as the greatest barriers to implementing personalized medicine services.

The aim of this project was to present several therapies and possible applications of these in the field of personalized medicine along with the production techniques and workflows surrounding them. There are two main categories; cell therapies and non-cell therapies. Cell therapies utilize the body's own T cells and immune system, and non-cell therapies are mostly based on proteins and nucleotides. All of these applications face different challenges that need to be overcome to be considered effective treatments and they all have a high production cost. The report also presents differences and similarities of manufacturing models that are specifically used in the production of cell therapies. It could be argued that these manufacturing models can be adjusted and work for both cell therapies and non-cell therapies. Three different workflows for three different personalized medicines, antibody drug conjugates (ADCs), tumor infiltrating lymphocytes (TILs) and mRNA vaccines, are presented in this report. Technologies and processes valuable to the manufacturing process were also presented, including bioreactors, interleukin 2 media and cell dissociation technologies. In conclusion, there are methods and techniques that are frequently used in production that are, or possibly could be useful for manufacturing personalized drug components. Production of products used in personalized medicine is possible if the right resources are available. Personalized therapies are presently most commonly applied to cancer diseases but there are developments for these therapies that could benefit several other diseases. To fully apply personalized therapies to these diseases further studies on suitable biomarkers and targets in drugs are needed. Overall, personalized medicine has promising possibilities in treatments for many types of complex diseases. This project was assigned by Cytiva which is a global life science company and the product order can be seen in the appendix.

Anticipations of personalized medicine are primarily attributed to the recent advances in computational science and high-throughput technologies that enable the ever-more realistic modeling of complex diseases. These diseases result from the interplay between genes and environment that have limited our ability to predict, prevent, or treat them. While many envision the utility of integrated high-dimensional patient-specific information, basic research towards developing accurate and reliable frameworks for personalized medicine is relatively slow in progress. This thesis provides a state-of-the-art review of current challenges towards personalized medicine. There is a need for global investment in basic research that includes 1) cost-effective generation of high-quality high-throughput data, 2) hybrid education and multidisciplinary teams, 3) data storage and processing, 4) data integration and interpretation, and 5) individual and global economic relevance; to be followed by global investments into public health to adopt routine personalized medicine. This review also highlights that unknown or unadjusted interactions result in true heterogeneity in the effect and relevance of patient data. This limits our ability to integrate and reliably utilize high-dimensional patient-specific data. This thesis further investigates the true heterogeneity in marginal effects of known BMI genetic variants. This involved the development of the novel statistical method, meta-quantile regression (MQR), to identify variants with potential gene-gene / gene-environment interactions. Applying MQR on public and local data (75,230 European adults) showed that FTO, PCSK1, TCF7L2, MC4R, FANCL, GIPR, MAP2K5, and NT5C2 have potential interactions on BMI. In addition, a gene score of 37 BMI variants shows that the genetic architecture of BMI is shaped by gene-gene and gene-environment interactions. The computational cost of fitting MQR models was greatly reduced using unconditional quantile regression. The utility of MQR was further compared to variance heterogeneity tests in identifying variants with potential interactions. MQR tests were found to have a higher power of detecting synergetic and antagonistic interactions for skewed quantitative traits while maintaining nominal Type I error rates compared to variance heterogeneity tests. Overall, MQR is a valuable tool to detect potential interactions without imposing assumptions on the nature of interactions. / Thesis / Doctor of Philosophy (PhD) / The anticipations of personalized medicine are largely due to the recent advances in computational science and our capabilities to rapidly measure and generate biological data. These developments have enhanced our understanding of complex diseases, and should theoretically enable us to predict, prevent and treat such cases in a proactive personalized context. This thesis provides a state-of-the-art review of the challenges and opportunities that explain the relatively slow progress towards personalized medicine. It identifies data integration and interpretation as the main bottleneck and proposes a novel method, termed Meta-Quantile Regression (MQR), to identify genetic variations with potential interactions. Analyzes were conducted on a total of 75,230 individuals with European ancestry, and the genetic architecture of obesity was shown to be shaped by genetic interactions. Lastly, the computational cost of MQR was substantially reduced using linear approximations, and MQR was further shown to have better performance in identifying potential interactions compared to classic variance tests.

This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient’s ability to engage in their own healthcare decisions. The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider’s decision on whether this test would provide beneficial information in selecting a patient’s medication. Model analysis shows that healthcare providers’ primary focus is to maximize patient health outcomes while considering the impact the patient’s economic welfare. The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC. The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why – whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies. Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes.