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1 - 10 of 38 results for: EPI ; Currently searching offered courses. You can also include unoffered courses

EPI 199: Undergraduate Research

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit (up to 99 units total)

EPI 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, MED 206, STATS 211)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win | Units: 3

EPI 214: Scientific Writing

(Formerly HRP 214) Step-by-step through the process of writing and publishing a scientific manuscript. How to write effectively, concisely, and clearly in preparation of an actual scientific manuscript. Students are encouraged to bring a manuscript on which they are currently working to develop and polish throughout the course. Please note 3-units students will additionally write and revise a manuscript.
Terms: Win | Units: 2-3

EPI 216: Analytical and Practical Issues in the Conduct of Clinical and Epidemiologic Research

(Formerly HRP 216) Topics include: advanced aspects of study design and data analyses; evaluating confounding and interaction; modeling continuous characteristics of exposure; building prediction models; methods of summarizing literature and quantifying effect sizes (meta-analysis); handling missing data; and propensity score methods. 3 units requires a data analysis project. Prerequisites: 258 or 261, or consent of instructor
Terms: Spr | Units: 2-3
Instructors: Popat, R. (PI)

EPI 219: Evaluating Technologies for Diagnosis, Prediction and Screening

(Formerly HRP 219) New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples o more »
(Formerly HRP 219) New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessments and original methods papers are used. Knowledge of statistical software is not required, although facility with at least Excel for basic calculations is needed. Open to students with an understanding of introductory biostatistics, epidemiologic and clinical research study design. Undergraduates may enroll with consent of instructor.
Terms: Win | Units: 3
Instructors: Goodman, S. (PI)

EPI 223: Introduction to Data Management and Analysis in SAS

(Formerly HRP 223) Provides hands-on introduction to basic data management and analysis techniques using SAS. Data management topics include: Introduction to SAS and SAS syntax, importing data, creating and reading SAS datasets, data cleaning and validation, creating new variables, and combining data sets. Analysis techniques include: basic descriptive statistics (e.g., means, frequency) and bivariate procedures for continuous and categorical variables (e.g., t-tests, chi-squares).
Terms: Aut | Units: 2

EPI 224: Genetic Epidemiology (GENE 230)

This course presents fundamental concepts and methods in genetic epidemiology, with examples on genetic studies of chronic diseases, including cancer, cardiovascular disease, metabolic conditions, and autoimmune diseases. It will provide an overview of various study designs, including family studies, and it covers fundamental analyses, inferences, and their strengths and limitations. It will include topics such as assessing genetic influences on disease; advances in genomics technology; family based study designs for linkage, exome sequencing and case-parent trios; candidate gene and genome-wide association studies of both common and rare genetic variants; gene-environment interactions, epistasis and non-Mendelian genetics; software and web-based data resources; ethical issues in genetic epidemiology; and applications of genetic epidemiology to clinical practice and public health. Guest speakers will discuss these concepts through the lens of various chronic diseases. Prerequisite: introductory biostatistics or epidemiology, biology, and genetics. Biostatistics (intro) or epidemiology (intro), biology, genetics (intro)
Terms: Win | Units: 3
Instructors: Hsing, A. (PI)

EPI 225: Introduction to Epidemiologic and Clinical Research Methods

(Formerly HRP 225) The skills to design, carry out, and interpret epidemiologic studies, particularly of chronic diseases. Topics: epidemiologic concepts, sources of data, cohort studies, case-control studies, cross-sectional studies, sampling, measures of association, estimating sample size, and sources of bias. Prerequisite: A basic/introductory course in statistics or consent of instructor.
Terms: Aut | Units: 3

EPI 226: Intermediate Epidemiologic and Clinical Research Methods

(Formerly HRP 226) The principles of study design, measurement, confounding, effect modification, and strategies for minimizing bias in clinical and epidemiologic studies. Prerequisite: 225 or consent of instructor.
Terms: Win | Units: 3
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