Course Descriptions

This course is an introduction to the fundamental concepts in statistics and their use in clinical research. Through class lectures, in class demonstrations, directed in class exercises and discussion of representative research reports from peer-reviewed journals, students are introduced to the core concepts in statistics, including: composition of data sets, descriptive statistics, hypothesis formulation, statistical significance, confidence intervals, statistical power, common statistical tests and basic statistical models. Basic statistical computations and introductory data analysis will be performed using R, a multi-platform (Windows, UNIX, Mac OS), free software environment for statistical computing and graphics. Prerequisite: None. Credit: 4. (Degree Requirement) LMS: Sakai

The emphasis is on general principles and issues in clinical research design. These are explored through the formulation of the research objective and the research hypothesis and the specification of the study population, the experimental unit, and the response variable(s). The course provides a basis for understanding the classification of studies as experimental or observational, prospective or retrospective, case-control, cross-sectional or cohort; this includes the relative advantages and limitations and the statistical methods used in analysis of each type.  Emphasis is placed on the traditional topics of clinical epidemiology such as disease etiology, causation, natural history, diagnostic testing and the evaluation of treatment efficacy. Prerequisite: None. Credit: 4. (Degree Requirement) LMS: Sakai

Coverage is provided of the fundamental knowledge in human genetics and genetic systems of the mouse and other model organisms. Topics include: introduction to concepts of inheritance (DNA, chromatin, genes, chromosomes); the human genome (normal genetic variation, SNPs, comparative genomes, molecular mechanisms behind inheritance patterns, and mitochondrial genetics); mouse genetics (classical mouse genetics, genotype- and phenotype-driven approaches, QTL mapping); microarrays (expression, genomic, ChIP (chromatin IP on chip), bioinformatics and use of genome databases); genetic association studies (haplotype blocks, study design in complex disease and approaches to complex disease gene identification, pharmacogenetics and pharmacogenomics).  Prerequisite: None. Credit: 2. LMS: Sakai

This course extends CRP 241 (Introduction to Statistical Methods) and primarily considers statistical models with a single predictor, to models containing multiple predictors.  We cover models with continuous outcomes (regression, analysis of variance, analysis of covariance), dichotomous outcomes (logistic regression), and time to event outcomes (survival models)  and count outcomes (Poisson and negative binomial models). Through class lectures, in class demonstrations, directed in class exercises, and discussion of representative research reports from peer-reviewed journals, students are introduced to the core concepts in statistical modeling. Prerequisite: 241. Credit: 4. (Degree Requirement) LMS: Sakai

This seminar integrates and builds on the core courses (CRP 241, 242, 245) to provide practical experience in the development and critique of the methodological aspects of clinical research protocols and the clinical research literature. Assigned readings are drawn from contemporary literature and include both exemplary and flawed studies. Prerequisite: None. Credit: 2. LMS: Sakai

Fundamental concepts in the design and analysis of clinical trials are examined. Topics include protocol management, sample size calculations, determination of study duration, randomization procedures, multiple endpoints, study monitoring, and early termination. Prerequisite: CRP 245. Credit: 2. LMS: Sakai

This course explores a variety of ethical and related issues that arise in the conduct of medical research. Topics include human subjects and medical research, informed consent, ethics of research design, confidentiality, diversity in medical research, international research, relationships with industry, publication and authorship, conflict of interest, scientific integrity and misconduct, intellectual property and technology transfer, and social and ethical implications of genetic technologies and research.  This course is designed to meet and exceed the NIH requirement for training in Responsible Conduct of Research.  Prerequisite: None. Credit: 2. (Degree Requirement) LMS: Sakai

This course addresses operational issues that arise in the conduct of a clinical research project. Topics include administration (human resources, project management, budget development and management), data management systems (databases, case report forms, data acquisition, quality assurance and quality control [QA/QC], monitoring and auditing), regulation (Investigational New Drug [IND]) applications, good clinical practice [GCP], and the Health Insurance Portability and Accountability Act [HIPAA]), and sponsorship (sources, sponsor motivations, identification of sponsors). Prerequisite: CRP 242. Credit: 2. (Degree Requirement) LMS: Sakai

Platform technologies and computational methodologies for protein profiling and interaction analysis are introduced. The platform technologies covered include mass spectroscopy, 2D gel electrophoresis, surface plasmon resonance, protein arrays and flow cytometry. Structural biology and high-throughput screening methods are also discussed.  Prerequisite: None. Credit: 2. LMS: Sakai

Modeling the potential impact of a health intervention on disease outcomes can be extremely useful in gaining an understanding of the underlying biology or epidemiology of a disease, in designing research studies, and in assessing whether an intervention is economically feasible. This course focuses on basic modeling techniques, with an emphasis on decision analysis and cost-effectiveness analysis, and the application of these techniques to the student's own research. Topics covered include basic decision theory, basic principles of economic analysis in health care, decision trees, Markov models, infectious disease models, and economic analysis of clinical trials, how to review a decision/cost-effectiveness analysis, and the application of models for research and policy analysis. Prerequisite: CRP 242.  Credit: 2.  LMS: Sakai

This course provides a practical foundation for systematic reviews involving quantitative synthesis (quantitative meta analysis). Through directed exercises, students learn when and how to perform quantitative synthesis using freely available software. Topics include: computing effect sizes, computing a combined effect, fixed effect vs. random effects analyses, heterogeneity in effect sizes, and methods to detect publication bias. Note: This course is offered in even-numbered years only.  Prerequisites: CRP 241 and CRP 242. Credit: 2. LMS: Talent

Longitudinal methods are required in the analysis of two types of study designs, those that involve questions about systematic change over time and those that involve questions about whether and when events occur.  The first type is characterized by repeated observations of the same variables over time, allowing the analysis of temporal changes.  In the second type, commonly referred to as time-to-event designs, the outcome of interest is the time to an event such as death or hospitalization.  The course covers the design, analysis and interpretation of these types of studies.  Various models, methodological issues and methods of analysis are discussed and demonstrated using R, SAS and Enterprise Guide.  Lectures are supplemented with readings from texts and journal articles.  Prerequisite: CRP 245. Credit 2 LMS: Sakai

This course introduces basic concepts of immunology, clinical assessment of immune function, and the fundamental importance of immune mechanisms in human disease. Topics include innate and adaptive immunity, regulatory mechanisms, and inflammation. Translational techniques used in immune   assessment are described in the context of relevant clinical examples. Emphasis is placed on the application of basic immunology to human diseases in oncology, infections, autoimmunity, and transplantation. Prerequisite: None. Credit: 2

This course provides students a foundation in the design of rigorous non-randomized studies that compare the effectiveness of one or more treatments to another. In addition to a brief history of comparative effectiveness research (CER), the course will use examples from the literature to highlight the strengths and weaknesses of CER against the gold standard randomized controlled trial (RCT). Through course readings, in-class discussions, and development of a short proposal on a non-randomized study of the students’ choosing, students will develop research skills and competencies related to understanding, conducting and interpreting non-randomized studies. Topics include: conceptual models, critical review of clinical literature, national survey and claims data sources, quasi-experimental study designs, sensitivity analysis and statistical adjustment in quasi-experiments, controlling for bias in observational data, and heterogeneity of treatment effects. Offered odd numbered years only. Prerequisite CRP 242. Credit: 2  LMS: Sakai

An individualized research project under the direction and supervision of the student's mentor and examining committee forms the basis for this culmination of the program of study leading to the degree. Credit: 12. (Degree Requirement)

This Research Project course is designed to provide a formal, structured, mentored environment in which students can practice skills necessary for conducting basic research. Students will work in their mentor’s research space on an individual research project chose and designed by the student with guidance from their mentor. Course directors will guide students in the selection of a research mentor and the development of a scholarship oversight committee, which will meet regularly with the student to guide the project. Mentors will provide 1:1 guidance on the development and conduct of the research project over the course of 4 semesters. Prerequisite: None. Credit: 18.  LMS: Talent

This course covers how to develop a conceptual model of the health outcomes that are important to measure, select the most appropriate type of COA for measuring a given health outcome, find and evaluate existing COAs or develop a new COA,  and use scores from a COA to construct a meaningful trial endpoint. The course will include general discussions of qualitative and quantitative methods in developing and evaluating measures.  Prerequisite: None. Credits: 2.

Implementation research (1) seeks to understand the processes and factors that are associated with successful integration of evidence-based interventions within a particular setting (e.g., a worksite or school), (2) assesses whether the core components of the original intervention were faithfully transported to the real-world setting (ie, the degree of fidelity of the disseminated and implemented intervention with the original study), and (3) is also concerned with the adaptation of the implemented intervention to the local context.  This course provides an overview of methods for undertaking research and program evaluation within health services organizations and systems. A particular focus will be on healthcare products and how to evaluate their impact on various stakeholders whether individual patients, family, health care providers, healthcare systems, or policy makers.  In addition to methods, the course also provides "the state of the art" in research and evaluation through the review of major completed studies. Case studies of recent programs and technologies will be used. This course is recommended for students who will be carrying out policy research, social science research, or program impact evaluation within health delivery systems as well as developing and implementing programs to improve healthcare outcomes.  Prerequisite: None.  Credit: 2. LMS: Sakai

Using a “flipped classroom” design, this course will teach you how to conceptualize and develop a major research project into a fundable grant proposal. We will present a stepwise approach and structured exercises that guide you through all aspects of research project development, from defining a problem of importance, to developing an experimental plan, to writing a compelling NIH-style grant application. Within this course, each student will develop their own research project and proposal using best practices, proven approaches, and continuous feedback from peers and instructors. Pre-requisite: None. Credit: 2.  LMS: Talent

This course will be offered in Fall 2023. Data science and machine learning (ML) are now beginning to impact clinical medicine, with performance on some tasks, such as detection of skin cancer, exceeding that of experienced clinicians. This course is designed to introduce students to the data science techniques poised to disrupt clinical practice through foundational material and clinical case studies. Course content will provide students with an intuitive, applications-oriented foundation in these techniques while highlighting both their capabilities and current limitations. Students will be introduced to pitfalls commonly encountered when developing models for clinical data as well as relevant practical and ethical considerations. Prerequisite: An introductory course in statistics and/or probability; and prior use of statistical software (e.g. R, SPSS, SAS, Python) to manage data and run analyses. None.  Credit: 2. LMS: Talent

This course covers best practices and strategies for multiple forms of scientific communication including manuscripts, social media, posters, presentations, news interviews, and reports. Prerequisite: None. Credit: 2. LMS: Talent