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1 - 10 of 24 results for: EPI

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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 202: R Fundamentals for Health Research (CHPR 202)

This introductory course is a practicum in which students will learn the basics of R, a free, open-source statistical analysis software program, and use the programming language to analyze health datasets by application of classical statistical methods. A familiarity with basic descriptive and inferential statistics is required (completion of HRP 258/259, or concurrent enrollment in an appropriate statistics/biostatistics course). It is assumed that students will have no (or very little) prior experience with R. This course is a ¿flipped classroom¿ where lecture content will be viewed at home before in-class meetings with hands-on coding practice by each student on their own computers. Priority for enrollment given to CHPR masters students, who must enroll for a letter grade.
Terms: Win | Units: 1
Instructors: Park, L. (PI) ; Lee, J. (TA)

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
Instructors: Ioannidis, J. (PI) ; Kvarven, A. (PI)

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
Instructors: Sainani, K. (PI) ; Lara Lince, F. (TA)

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

EPI 224: Genetic Epidemiology (GENE 230)

This course presents fundamental concepts and methods in genetic epidemiology, with examples from genetic studies of common, complex diseases (e.g., cancer). It will provide an overview of various study designs and covers fundamental analyses, inferences, and their strengths and limitations. The course will cover the following topics: assessing genetic influences on disease (e.g., heritability); family- and population-based association study designs; candidate gene and genome-wide association studies of common and rare genetic variants; transcriptome-wide association studies; polygenic risk scores; bias due to population stratification; gene-environment interactions and epistasis; studies of diverse populations; 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 diseases. The course will include a project proposal based on student's research interests. Prerequisite: introductory biostatistics or epidemiology (or by permission of the instructor).
Terms: Win | Units: 3
Instructors: Kachuri, L. (PI) ; Witte, J. (PI)

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
Instructors: Benjamin-Chung, J. (PI) ; Nguyen, A. (TA)

EPI 229: Stanford CTSA Scholars Seminar

Preference to trainees awarded Stanford internal KL2, TL1 grants. Focus is on students and junior faculty who have received a CTSA KL2 or TL1 Award. Discussions include progress and challenges involved in starting and conducting clinical research, current courses, time management and resources; support from peers; education and professional development. All scholars are required to attend a weekly seminar series meeting throughout the year that will cover an array of cross-cutting methodological topics with published examples of implementation. Prerequisite: Awarded a CTSA KL2, TL1 Grant or Spectrum UL1
Terms: Aut, Win, Spr | Units: 1 | Repeatable 8 times (up to 8 units total)
Instructors: Asch, S. (PI) ; Felsher, D. (PI) ; Goodman, S. (PI)

EPI 231: Epidemiology of Infectious Diseases

(Formerly HRP 231) Principles of the transmission of the infectious agents (viruses, bacteria, rickettsiae, mycoplasma, fungi, and protozoan and helminth parasites). The role of vectors, reservoirs, and environmental factors. Pathogen and host characteristics that determine the spectrum of infection and disease. Endemicity, outbreaks, and epidemics of selected infectious diseases. Principles of control and surveillance.
Terms: Win | Units: 3
Instructors: Andrews, J. (PI) ; Maldonado, Y. (PI) ; Parsonnet, J. (PI)

EPI 236: Epidemiology Research Seminar

(Formerly HRP 236) Weekly forum for ongoing epidemiologic research by faculty, staff, guests, and students, emphasizing research issues relevant to disease causation, prevention, and treatment. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable 15 times (up to 15 units total)
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