Clinical Epidemiology

EPI 204 Fall 2019 (3 units)
Course Director: Michael Kohn, MD, MPP
Professor
Department of Epidemiology & Biostatistics

OBJECTIVES

This is primarily a course about prediction. In common speech, prediction involves using information that is available now to evaluate the likelihood of an uncertain event in the future. In epidemiology and biostatistics, “prediction” includes using available information not only to predict the future but also to estimate the probability of a state or condition that already exists but is difficult or expensive to measure by other means. In public health and clinical practice, diagnostic tests are used to estimate the probability of prevalent disease and risk prediction models (or “rules”) are used to evaluate the likelihood of some future event. In this course, we will cover how to interpret the metrics used to describe the performance of diagnostic tests and risk prediction models, how to design a research studies to evaluate tests and risk models, and how to use the results of tests and risk models to inform decision-making. Throughout, we assume that the information from tests and models guides decisions. Although the tests and models discussed are clinical and the decisions are often treatment decisions, the principles apply to any problem of prediction and decision-making under uncertainty.

The specific objectives of this course are to provide a basic understanding of:

  • sensitivity, specificity, predictive value
  • likelihood ratios, ROC curves
  • inter-observer agreement, reliability, and measurement error
  • calibration plots, net benefit calculations, decision curves
  • logistic regression, and recursive partitioning (at an introductory level)
  • special issues related to the evaluation of screening tests and programs
  • quantifying treatment benefits and harms using the result of randomized trials
PREREQUISITES

Designing Clinical Research (EPI 202). Exceptions may be made with the consent of the Course Director, space permitting. The course draws heavily upon clinical examples and may be more challenging for students without any clinical background. However, learning how to use clinical information to diagnose disease or predict outcomes and guide treatment decisions is an excellent way to introduce prediction in general.

FACULTY
Course Director:

Michael Kohn, MD, MPP
Phone: 415-514-8142
email: michael.kohn@ucsf.edu

Course Director Emeritus:

Thomas Newman, MD
email: newman@epi.ucsf.edu

Faculty Section Leaders:

Shabnam Peyvandi, MD, MAS
email: Shabnam.Peyvandi@ucsf.edu

 

Martina Steurer-Mueller, MD, MAS
email: martina.steurermuller@ucsf.edu

 

Miriam Laker-Oketta, MD, MSc
email: drmiriaml@yahoo.co.uk

Teaching Assistant Section Leaders: Kirk Fergus
email: kirk.fergus@ucsf.edu
  Kerstin Kolodzie, MD, PhD
email: kerstin.kolodzie@ucsf.edu
  Ryan McMahan
email: ryan.mcmahan@ucsf.edu
  Samuel Washington, MD
email: samuel.washington@ucsf.edu
FORMAT
  1. Lectures:
    Thursdays: 8:45 to 10:15 AM, September 20 through December 6. Lecture recordings will be available online later in the day.
  2. Small Group Sections:
    Content: Overview and discussion of lectures, and review of homework assignments.
    Time: Thursdays: 1:00 to 2:30 PM. beginning Sept. 20.

All course materials and handouts will be posted on the course's online syllabus.

MATERIALS

We are working on the second edition of our 12-chapter Evidence-Based Diagnosis textbook. We will post the required reading from this book on the course syllabus site.

Optional

Some of our material can also be found (in abbreviated form) in Designing Clinical Research, by Stephen B. Hulley, MD, MPH et al. Lippincott Williams & Wilkins. 4th Edition. 2013. Chapter 12 is particularly useful and is partly based on this course.

Stata Statistical Software (Stata Corporation, College Station, TX). Stata is not required for any of the homework or exam problems, but some students find it useful for the unit on inter-rater agreement. In this course, you may use whatever tools you like, including Excel, an online calculator, or Stata.

GRADING

Grading is based equally on homework (including the problem-writing assignment, which counts as 1 homework) and a take-home final exam.

Students not in full-year TICR Programs who satisfactorily pass all course requirements will, upon request, receive a Certificate of Course Completion.

UCSF Graduate Division Policy on Disabilities

TO ENROLL

This course is sponsored by the Training in Clinical Research (TICR) Program, and space is limited. Preference is given to UCSF-affiliated personnel. We regret that auditing is not permitted.

To apply for this course, please fill out and submit the application below. Please see our fees page for cost information. The deadline for application is September 3, 2019. Only one application needs to be completed for all courses desired during the quarter.

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