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Course Descriptions

CRP 241 Introduction to Statistical Methods

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)

CRP 242 Principles of Clinical Research

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. Corequisite: CRP 241. Credit: 4. (Degree Requirement)

CRP 243 Introduction to Medical Genetics

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.

CRP 245 Statistical Analysis

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: CRP 241. Credit: 4. (Degree Requirement)

CRP 247 Clinical Research Seminar

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.  Prerequisites: CRP 242 and CRP 245. Credit: 2.

CRP 248 Clinical Trials

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.

CRP 249 Health Services Research

Research methods in health services research are explored. Topics include measurement of health-related quality of life, case mix and comorbidity, quality of health care and analysis of variations in health care practice. Advantages and disadvantages of studies that use large databases as well as advanced methods in analysis and interpretation of health services outcomes are addressed. This includes application of traditional research designs (e.g., randomized trials) to address health services research questions and the interface between health services research and health policy. Prerequisite: None  Credit: 2.

CRP 253 Research Ethics and Responsible Conduct of Research

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)

CRP 254 Research Management

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)

CRP 257 Proteomics and Protein Biology in Medicine

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.

CRP 259 Decision Sciences in Clinical Research

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.

CRP 262 Systematic Reviews and Meta Analysis

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.

CRP 263 Longitudinal Data Analysis

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.

CRP 264 Introduction to Immunology in Clinical Research

Using a “flipped classroom” design, this course presents two major aspects of Immunology.  First, it provides an introduction to the fundamental concepts of immunology and its role in human disease in a series of videos that are downloaded and watched prior to attending the class.  Second, it provides a comprehensive review of cutting edge immune diagnostic and therapeutic strategies as they are applied to human disease in a series of student led in-class topic reviews and discussions.  Emphasis is placed on the application of immunology to oncology, infection, autoimmunity, and transplantation. The curriculum is customized to match the interests of the students enrolled. Prerequisite: None. Credit: 2

CRP 266 Design and Analysis of Non-Randomized Studies.

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. Prerequisite CRP 242 or permission of the instructors. Prerequisite: None. Credit: 2.

CRP 270 Research

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)

CRP 270 BST Research

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.

CRP 271 Patient-Reported Outcomes in Clinical Research

Patient-reported outcomes (e.g., fatigue, pain, physical functioning, social functioning, etc.) can provide great value to research but present significant challenges.  This course provides students with the knowledge necessary to incorporate patient-reported outcomes into observational studies and clinical trials.  Topics include the different types and suitability of measures, the development of new measures, and techniques for analyzing and interpreting patient-reported outcomes. Prerequisite: CRP 242. Credit: 2.

CRP 273 Implementation and Dissemination of Health Care Research

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.

CRP 275 Research Project and Proposal Development: A Stepwise Approach.

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.

CRP 276 Statistical Methodology for Basic Research

This course focuses on the appropriate application of core concepts taught in CRP 241 (Introduction to Statistical Methods) to the arena of basic science research, including dataset construction, descriptive statistics, hypothesis formulation and study power, and statistical inference. Through in-class lectures, directed exercises, and discussion of representative peer-reviewed manuscripts, students engage with core concepts in statistical modeling through its real-world application to the challenges of bench-science research. Classes will generally be delivered using a combination of brief introductory lectures followed by a journal club-format discussion in which students will be responsible for presenting and critiquing a peer-reviewed manuscript selected for its relevance to that week’s topic area (e.g. handing non-Gaussian continuous outcomes). At the end of the course, students will be able to think critically about study design, draft study power sections for grant proposals, and outline about study design, draft study power sections for grant proposals, and outline a statistical analysis plan that would be appropriate to share at a pre-study consultation session with a master’s or PhD-level staff biostatistician. Data analyses will be performed using R, a free software environment for statistical computing and graphical presentation. Prerequisite: 241. Credit: 2.

CRP 277 Research Professional Development

This course will be offered in Fall 2021. This course will cover practical aspects for early career scientists including, funding strategies and grant-writing, lab management, career negotiation, self- and scientific promotion, forming and maintaining collaborations, and authorship. Prerequisite: None. Credit: 2.

CRP 278 Machine Learning for Health

This course will be offered in Fall 2021. 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.

CRP 279 Scientific Communication

This course will be offered in Spring 2022. 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.