1 - 10 of 16 results for: EPI

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.

(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).

(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.

(Formerly HRP 227) Theory and applied methods for causal inference in epidemiology. Focus on the potential outcomes model and related methods including inverse probability weights, G-computation, and targeted maximum likelihood estimation. Other contemporary topics may be included. Learning is facilitated through in-class discussion, critical review of peer-reviewed literature, and applied laboratories in R. Prerequisites: EPI 225, EPI 226, and EPI 261 or equivalent (or permission of instructor).

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

(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.

The Intensive Course in Clinical Research (ICCR) is a one-week immersion course designed for new or aspiring clinical investigators, medical students, residents, graduate students, fellows and junior faculty interested in pursuing careers in clinical and transnational research. Students spend five days and four evenings immersed in all aspects of research study design and performance. The format combined didactic with intense group/team activities focused on practical issues in clinical research design - from selection of a researchable study question through actual writing of a research proposal. Lectures and panel discussions are presented by an accomplished faculty of Stanford clinical researchers and key leaders from the Stanford community. Every presentation includes a discussion of relevant issues. The course is supported by over 40 faculty and fellows from across the School of Medicine.

(HUMBIO students must enroll in HUMBIO 57. Med/Graduate students must enroll in EPI 247.) We will cover: the components of public health systems in the US; principles of outbreak investigation and disease surveillance; different types of study design for field investigation; visualization and interpretation of public health data, including identification and prevention of biases; and implementation of disease control by public health authorities. Students will meet with leaders of health departments of the state and the county and will be responsible for devising, testing and evaluating a field questionnaire to better understand the complexities of field research. (Formerly HRP 247)

(HUMBIO students must enroll in HUMBIO 89X. Med/Graduate students must enroll in EPI 259.) Topics: random variables, expectation, variance, probability distributions, the central limit theorem, sampling theory, hypothesis testing, confidence intervals. Correlation, regression, analysis of variance, and nonparametric tests. Introduction to least squares and maximum likelihood estimation. Emphasis is on medical applications. (Formerly HRP 259)