Doctor of Philosophy, Case Western Reserve University, 2022, Clinical Translational Science

The translation of medical therapies from basic and preclinical research to efficacious human interventions is challenging. The majority of candidate therapies fail in early-stage human trials, after showing promise in preclinical work. The primary aim of the research presented herein is to explore the potential role that poor statistical practice in preclinical animal trials might play in contributing to translational failure. First, a comprehensive appraisal of current statistical practice in one area of preclinical neuroscience research was carried out. A close review of the current related literature is presented, and the appraisal includes a tutorial to explain how certain statistical mistakes might result in overly optimistic results, as well as practical recommendations for improvement. A majority of articles included in this appraisal failed to account for sources of non-independence in the data (74-93%) and/or did not analytically account for mid-treatment animal attrition (78%). Ordinal variables were often treated as continuous (37%), outliers were predominantly not mentioned (83%), and plots often concealed the distribution of the data (51%). Next, a sample including both successful and failed human trials for neurologic targets was identified, and rates of statistical mistakes in the associated preceding rodent trials were compared. Failed human trials were found to have higher rates of select sources of potential statistical bias in preceding rodent trials, compared to successful trials. This research provides evidence that a contributing factor to translational failure is statistical misapplication in preclinical animal research in the neurosciences. It provides the groundwork for future research that will provide practical solutions to translational researchers and funders, facilitating preclinical experimental validity to increase the translational success rate.

Committee: Mary Dolansky PhD RN FAAN (Committee Chair); Kenneth Baker PhD (Committee Member); Nancy Obuchowski PhD (Committee Member); Jill Barnholtz-Sloan PhD (Advisor) Subjects: Animal Sciences; Biostatistics; Neurosciences; Statistics

Master of Science, The Ohio State University, 2024, Public Health

Multi-arm trials are common among randomized controlled trials. These parallel-group trials compare three or more interventions, usually to a shared control. Our simulation study focuses on a specific multi-arm structure – one that involves three arms: control, treatment, and treatment plus. Due to the potential number of comparisons that investigators can conduct under this three-arm structure, the issue of multiple comparisons must be considered. For a three-arm treatment plus trial, seven methods were explored: Bonferroni, Holm, Hochberg, fixed sequence, hierarchy, Dunnett, and Prospective Alpha Allocation Scheme (PAAS). To directly compare these procedures with one another, a simulation study was conducted to determine which method controlled FWER at α = 0.05 while maximizing power. Power for each method was calculated as sample sizes, allocation schemes, prevalences and effect sizes were changed. Overall, we saw that all seven methods could maintain the expected α = 0.05 type I error rate. Dunnett generally performed the worst and was not recommended as a solution. Bonferroni, Holm, Hochberg, and PAAS were comparable in power while fixed sequence and hierarchy varied depending on allocation ratio.

Committee: Rebecca Andridge (Committee Member); Abigail Shoben (Advisor) Subjects: Biostatistics

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Data Science

The use of statistical and machine learning methods in collection, evaluation and presentation of biological data is very extensive. This reflects a need for precise quantitative assessment of different types of challenges encountered in the field of healthcare. But the sparse nature of medical data makes it hard to find the hidden patterns and as a result makes the prediction a complex task. This dissertation research discusses several biostatistical methods including sample size determination in a balanced clinical trial, finding cohort risk from case control information, odds ratio, Cochran-Mantel-Haenszel odds ratio etc. along with examples and analysis of a real life dataset to further solidify the concepts. Moreover, different classification models: Random Forest, Gradient Boosting, Support vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression, Artificial Neural Network (ANN) are applied in the analysis of Wisconsin Breast Cancer (diagnostic and original) dataset and their performance comparison is presented. Later, these classification models are also used in conjunction with ensemble learning methods; since ensemble methods significantly improves the predictive outcomes of the classification models. The evaluation of the classification models is measured using accuracy, AUC score, precision and recall metrics. In tree-based classification models, Random Forest (solely and in conjunction with the ensemble learning) gives the highest accuracy; whereas in the later chapter Artificial Neural Network gives the highest accuracy measure.

Committee: John Chen Ph.D. (Committee Chair); Mohammadali Zolfagharian Ph.D. (Other); Umar Islambekov Ph.D. (Committee Member); Qing Tian Ph.D. (Committee Member) Subjects: Biostatistics; Statistics

Doctor of Philosophy, Case Western Reserve University, 2024, Bioethics and Medical Humanities

Family caregivers are influential in medical decision-making for cancer patients, yet little is known regarding how they are included in clinical trial decisions. Clinical trial staff are responsible for informed consent in oncology trials and maintain ongoing contact with patients and caregivers, yet they are also overlooked in clinical trial decision-making research. This study fills this knowledge gap by eliciting perspectives from family caregivers and clinical trial staff, key stakeholders in clinical trial decision-making. The specific aims were: (1) to understand how family caregivers of patients with cancer conceptualize their role in clinical trial decision making, (2) to determine factors influential to family caregivers of patients with cancer during the clinical trial decision-making process, and (3) to identify attitudes, beliefs, and approaches used by clinical trial staff to navigate family caregiver involvement in the informed consent process for clinical trials. I conducted focus groups with 10 clinical trial staff and 9 caregivers of patients with cancer, then 15 caregivers participated in semi-structured interviews. Focus group data were used to refine interview guides for subsequent semi-structured interviews with caregivers. Transcripts were coded, then analyzed with NVivo using content analysis. Caregiver themes included promoting patient autonomy, influential factors, and burdens of trial participation. Trial staff themes were approaches to caregiver inclusion, caregiver utility for consent and adherence, and the need for training. Caregivers viewed their role as supporting patient understanding and deferring to the patient as final decision-maker. Hope for therapeutic benefit, oncologist endorsement, and practical barriers (e.g. cost, distance) were influential. Trial staff viewed caregivers as highly influential and relied on them to enhance patient understanding and adherence to the trial protocol. Staff experienced occasional challenges (open full item for complete abstract)

Committee: Sana Loue (Committee Chair); Jennifer Dorth (Committee Member); Barbara Daly (Committee Member); Mark Aulisio (Committee Member) Subjects: Medical Ethics; Oncology

PhD, University of Cincinnati, 2023, Business: Business Administration

The three chapters presented in this work focus on improving statistical inference for time series models and clinical trial data. In the first chapter, a Bayesian approach is proposed for testing serial error independence in time series models, addressing the Lindley paradox and outperforming frequentist tests in AR1 models. The second chapter provides simulation evidence comparing conventional Mixed Models for Repeated Measures (MMRM) with Bayesian hierarchical models that explicitly specify dynamic correlation, showing improved predictive accuracy and reliable treatment effect estimation in small samples. The third chapter applies the best-performing model specifications from the previous chapter to clinical trial data, highlighting the importance of socioeconomic factors in treatment outcomes for major depressive disorder. The first chapter addresses the assumption of serial independence of model approximation errors in time series modeling, which is crucial for reliable inference. While frequentist methods like the Ljung-Box and Breusch-Godfrey tests have been widely used for hypothesis testing, the increasing popularity of Bayesian Inference demands comparable Bayesian testing procedures. However, Bayesian hypothesis testing has faced challenges in resolving the Lindley paradox, resulting in contradictory inference when compared to frequentist tests. To address this issue, we propose a Bayesian procedure for testing serial error independence in time series models, which enables paradox-free inference. The results demonstrate that the proposed Bayesian test outperforms frequentist tests in Autoregressive Order 1 (AR1) models in small to moderate-sized samples and performs comparably well in higher order autoregressive models. Clinical trials are often costly and have limited observations, making it crucial to achieve precise inference about treatment effects in small samples. The Mixed Models for Repeated Measures (MMRM) have been the standard to (open full item for complete abstract)

Committee: Jeffrey Mills Ph.D. (Committee Chair); Hans Breiter M.D. (Committee Member); Jeffrey Strawn (Committee Member); Olivier Parent Ph.D. (Committee Member); Lenisa Chang Ph.D. (Committee Member) Subjects: Statistics

DNP, Kent State University, 2023, College of Nursing

Unintended pregnancies cost an estimated $12 billion annually in publicly funded benefits, contributes to intergenerational poverty, and results in lower educational attainment for mothers and their children. Several professional organizations recommend all health care providers counsel women regarding contraception at every visit regardless of the reason for the appointment. However, contraceptive services provided in any setting is considered inadequate overall (ACOG, 2022; CDC, 2016). This quality improvement project used the plan, do, check, act methodology to assess patient satisfaction with a provider assisted digital contraception education tool. Assessment was also done to ensure that it does not create any type of burden for the provider. Data collection included patient surveys regarding satisfaction with tool use at the conclusion of the visit. Provider perception of helpfulness of bedsider.org will be assessed using a survey at the end of the data collection period. Support staff perceptions of disruptions in office workflow will also be assessed via a one question survey at the end of the data collection period. The convenience sample included 100 patients scheduled for an appointment for the purposes of contraceptive counseling during the designated data collection periods. The provider sample was limited to the physician provider and the nurse practitioner in the office setting. Ancillary staff (medical assistants, secretaries, schedulers) were also be asked to complete a survey regarding the extent of office workflow disruption. The digital contraception educational tool was found to be satisfactory for patients, providers, and ancillary staff.

Committee: Eldora Lazaroff (Committee Chair); Constance Cottrell (Advisor); Karen Mascolo (Committee Member); Lynn Gaddis (Committee Member) Subjects: Nursing

Master of Arts, The Ohio State University, 2021, Bioethics

The ethical issues associated with randomized controlled trials (RCTs) and the tensions between clinical care and clinical research are amplified in times of public health crises. To illustrate, this thesis will focus on two types of research cases related to COVID-19 RCTs. Each case, one pertaining to clinical treatments and another pertaining to vaccinations, will be presented and the relative ethical issues that each case raises will be teased out. Intrinsic conflicts are present in the physician-researcher role due to the nature of opposing responsibilities physician-researchers are tasked with; they are responsible for the welfare of patients as well as the integrity of research processes and results. Trust within the relationship between the physician-researcher and patient-subject is paramount to avoid the exploitation of patient-subjects as a vulnerable population. It is important for patient-subjects to trust that physician-researchers are concerned with protecting best interest. The fact that physician-researchers are exposed to an abundance of conflicts of interest when inviting patient-subjects to partake in clinical trials adds multiple layers of ethical complexity. This thesis will also explore the relationship between equipoise and clinical research, delving into the question of when it is ethically permissible for physician-researchers to enroll patient-subjects in RCTs. Rather than regarding Fried's equipoise and clinical equipoise as competing concepts, this thesis will argue for a conceptualization that sees Fried's equipoise and clinical equipoise be applied in a complementary manner, which will support the preservation of the integrity of the physician-researcher role. Furthermore, analysis of the COVID-19 research cases will exemplify how the concept of equipoise has limitations and holds varying degrees of relevance depending on the context.

Committee: Dana Howard PhD (Advisor); Courtney Thiele JD, MA (Committee Member); Karla Zadnik OD, PhD (Committee Member) Subjects: Medical Ethics

Doctor of Philosophy, Case Western Reserve University, 2019, Epidemiology and Biostatistics

Oncology clinical trials generate the evidence required to obtain regulatory approval for new interventions; the life-saving treatments cancer patients receive today, and the novel therapies that will transform future care paths, rely on data from clinical trials. Unfortunately, the therapeutic advances driven by clinical research are limited to the patient populations that best represent those enrolled in clinical trials. The explicit driver of this limited generalizability is the design of clinical trial eligibility criteria. Although overly restrictive eligibility criteria have been critiqued in the literature, quantitative studies evaluating the appropriateness of these criteria have not been performed. Therefore, we analyzed the eligibility criteria of a particular oncology disease group (hematologic malignancies), specifically exploring: 1) the relationship between commonly used organ function eligibility criteria and the expected toxicities of the trials' interventions, 2) reasons for ineligibility and the outcomes of leukemia patients eligible vs. ineligible for South Western Oncology Group (SWOG) trials, and 3) the health policy implications of overly restrictive eligibility criteria. Collectively, the findings of these studies suggest that the eligibility criteria for hematologic malignancy clinical trials are overly restrictive; the organ function criteria fail to reflect the expected toxicities of the trials' interventions / observed adverse events and the administrative criteria associated with the timing of screening tests / sample submissions included in SWOG leukemia protocols are too conservative. Furthermore, our results demonstrated the safety and efficacy outcomes were comparable between the leukemia/myelodysplastic syndrome patients ineligible for administrative or non-clinically significant reasons and the patients fully meeting the eligibility criteria. These findings suggest, patients who may benefit from potentially life-saving treatmen (open full item for complete abstract)

Committee: Siran Koroukian PhD (Advisor); Dana Crawford PhD (Committee Chair); J.B. Silvers PhD (Committee Member); Mikkael Sekeres MD, MS (Committee Member) Subjects: Design; Health Care; Health Sciences; Medicine; Oncology; Public Policy

Master of Science, The Ohio State University, 2018, Food Science and Technology

There are various steps involved in functional food research and development that begin with identifying and characterizing a bioactive of interest, designing a specific food vehicle, optimizing the product, scaling up production for clinical trials, recruiting for and conducting a clinical trial, and addressing any challenges that occur. Most food science studies focus on the initial food development with little research on the requirements for effective functional food clinical studies or public health outcomes. Clinical trial results are often dependent on the dose of bioactive that a subject receives and it is of the utmost importance to ensure that each batch of scaled-up product delivers a comparable amount of bioactive. In the case of this study, the bioactive of interest was soy isoflavones delivered in the form of a soy soft pretzel snack. Various manufacturers were contacted for a contract manufacturing opportunity in order to mass-produce soy soft pretzels. Of three manufacturers that agreed to produce a test batch, one company has been able to recreate the sponge and dough process and a soy pretzel with an isoflavone concentration that is comparable to the bench top pretzel. Communications with this company will continue into the future as smaller “on-the-go” pretzel pieces are explored. Once a functional food has been produced, it can be assessed in a clinical setting. In the case of the soy soft pretzel, the cohort of interest was HIV+ individuals with high cholesterol and inflammation. HIV+ individuals are often afflicted with dangerously high cholesterol as a result of anti-retroviral therapy drugs (ART) that are prescribed to lower the viral load of HIV. Further, HIV+ individuals often live with chronic inflammation as a result of trace amount of the virus circulating throughout their system. Rather than prescribing additional medication to address these issues, a functional food approach was selected. The goal was to conduct a 6-week single-a (open full item for complete abstract)

Committee: Yael Vodovotz (Advisor); Nicholas Funderburg (Committee Member); Devin Peterson (Committee Member) Subjects: Food Science; Nutrition

Doctor of Philosophy, The Ohio State University, 2002, Graduate School

Committee: Not Provided (Other) Subjects: Biology

Doctor of Philosophy, The Ohio State University, 2016, Computer Science and Engineering

Clinical trials are instrumental in translating outcomes of scientific research into medical practice. Enrollment of patients that meet requirements of a significant sample size, within a desired time span, is limited by the speed and efficiency of screening patients for these trials. The current process of eligibility screening involves repeated reading of clinical notes to evaluate patients against intricate eligibility criteria. Significant time, human effort and financial resources are consumed to accomplish this task. This dissertation analyzes possible reasons that limit the efficiency of the current clinical trial screening workflow and presents automated methods to facilitate and expedite the enrollment process. This includes extracting specific medical concepts from clinical notes, the study and reasoning of vague language used by healthcare professionals, and finally the identification of criteria-relevant text in clinical notes. We also show that active learning and semi-supervised learning techniques help in overcoming the challenge of limited and expensive training data in this domain.

Committee: Eric Fosler-Lussier (Advisor); Lai Albert (Committee Member); Marie-Catherine de Marneffe (Committee Member); Ritter Alan (Committee Member); Mills Monique (Committee Member) Subjects: Biomedical Research; Computer Engineering; Computer Science; Linguistics

Master of Sciences, Case Western Reserve University, 2014, EECS - Computer and Information Sciences

Clinical trials play an important role in drug development and improving patient care. A common reason for clinical trials to fail is the insufficient number of subjects recruited in a reasonable time frame. Recent study demonstrated that electronic patient screening could improve the efficiency of clinical trial participant recruitment. Trial Prospector is a new screening system for matching patients with cancer clinical trials. It has been developed by a multi-disciplinary team of researchers from Case Western Reserve University, Case Comprehensive Cancer Center, and Clinical and Translational Science Collaborative of Cleveland. It promotes a unique preemptive workflow in trial recruitment strategy, which has been piloted at the Seidman Cancer Center. The main contribution of this thesis is an ontology-driven interface called VISAGET for Trial Prospector, for creating computable models of clinical trial eligibility criteria. VISAGET addresses an important barrier in electronic screening of eligible patients for clinical trials in the accurate representation of inclusion and exclusion criteria as computable and executable queries. Adapted from the Physio-MIMI query interface VISAGE, it uses standard terminologies such as the NCI Meta-thesaurus to support both the authoring and editing interface for eligibility representation, as well as for mapping of patient database and translated trial eligibility database. The VISAGET front-end allows nurses, physicians, and study coordinators to select any subset of trials for screening any subset of available patients. The matching results are automatically generated in a user-friendly report format, with easy access to raw data elements demonstrating the reason behind the results. For testing the expressiveness and usability of VISAGET, a total of 15 active trials have been captured for the screening of 93 patients. Preliminary evaluation has been performed in two ways: for the accuracy in eligibility criteria representatio (open full item for complete abstract)

Committee: Guo-Qiang Zhang Dr. (Committee Chair); Satya Sahoo Dr. (Committee Member); Rong Xu Dr. (Committee Member) Subjects: Biomedical Research; Computer Science

Master of Science, The Ohio State University, 2012, Public Health

In randomized clinical trials, the presence of missing data presents challenges in determining the actual treatment effect of the study. It is particularly problematic in longitudinal studies when patients followed over time withdrawal from the study. Although it is important to anticipate and attempt to prevent these drop-outs in the study design, it is still likely that a significant amount of missingness will be present in the final data. It is important to have statistical methods that effectively analyze data which contains missing values, and produce unbiased results. This study compares several methods for handling missing data in longitudinal trials. The focus is on the single imputation method of last observation carried forward, and compares it to complete case analysis, multiple imputation and two additional versions of multiple imputation where everyone was imputed as if they were actually in the control group (placebo-imputation). We simulated a randomized control trial with a treatment and placebo group and two time points. After creating the data, we imparted missingness in the follow-up time point. We considered three mechanisms for the missing data: missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). The results indicated that in all situations, last observation carried forward produced extremely biased estimates of treatment effect. Both placebo imputations produced similarly biased estimates. Complete case analysis was only valid in the situation where the data was MCAR. Traditional multiple imputation using regression performed the best of all the methods.

Committee: Rebecca Andridge PhD (Advisor); Abigail Shoben PhD (Committee Member) Subjects: Biostatistics

Doctor of Philosophy (PhD), Ohio University, 2004, Psychology (Arts and Sciences)

This methods paper overviewed the challenges in statistical analyses of clinical trials with continuous scale outcomes measures. The currently used statistical methods for this type of data were identified from clinical trials published between September 16, 2001 and September 15, 2002 in three respected journals: one from psychology, psychiatry, and medicine were reported. The strengths and limitations of the commonly used statistical models were examined. To address some problems that plague commonly used statistical methods for this type of data, an intrinsically nonlinear function was developed and implemented on a clinical trial dataset using nonlinear growth curve methodology. The results of the nonlinear growth curve model were compared to those of repeated measures ANOVA, the mixed model for repeated measures, and a polynomial linear growth curve model. For a clinical trial that evaluated outcomes from pharmacological and behavioral interventions for treating chronic tension-type headaches, the nonlinear growth curve model provided a better fit to the data based on Schwarz's Bayesian Information Criteria and Akaike's Information Criteria, more reasonable subject-specific and interpolated predicted values, and more clinically meaningful coefficients than the competing models. The four coefficients for the nonlinear function represented the baseline symptom level, the amount of change, and two different aspects of the rate of change. Despite the increased complexity in estimation, this nonlinear growth curve model appears to be viable alternative for analyzing clinical trial data.

Committee: Kenneth Holroyd (Advisor) Subjects: Psychology, Clinical

Master of Arts (MA), Bowling Green State University, 2006, American Culture Studies/Sociology

This thesis assesses clinical research involving human subjects as a social institution of global reach. Scientific, philosophical, and cultural dimensions of clinical research are examined for their contributions to the construction of trials, and two philosophical interpretations of scientific methodology are consulted for their views about the penetration of scientific theories by social values: Hugh Lacey's positive empiricist account of the role of cognitive values and social strategies in science, and Helen Longino's contextualist feminist theory of scientific inquiry, objectivity, and social knowledge. The socio-cultural construction of the conceptual and ethical structure of clinical research is emphasized. Ethical analyses of clinical research focus on the use of divergent normative standards for clinical trials in the developed and developing world. The dominant bioethical model offered for transcultural ethical research, principlism, is described and critically assessed. The transnational ACTG 076 clinical trials are presented as a case study of global research and bioethical evaluation. Martha Nussbaum's human capabilities model is proposed as an alternative, contexualist framework of clinical research ethics for its particular focus on the ethics and politics of distributive justice, which is a crucial issue in the contexts of health care and clinical research.

Committee: Kathleen Dixon (Advisor) Subjects: American Studies