Power and Sample Size for the Design of Epidemiological Studies I | Johns Hopkins

June 10 - June 19, 2024
Online Course
1 Credit
Course Number: 340.694.89

"Professor Kong was fantastic! I thought her lectures clear and her walk throughs were very helpful"—Student, 2023

"Absolutely stellar lectures that clearly explain complex topics. Very practical, goal-directed lessons. Straightforward evaluations"—Student, 2020

Course Instructor:

Xiangrong Kong

Description:

Systematically introduces students to sample size and power analysis for the most common epidemiological study designs. Provides participants with the key conceptual elements and practical tools for computing sample sizes to achieve a given level of precision and power in statistical tests.

Learning Objective(s):

Upon successfully completing this course, students will be able to:

Identify the factors which influence power and sample size, including the variability of the measurement and the desired precision of an effect estimate.
Calculate required sample sizes and minimal detectable difference for one- and two-sample hypotheses within common epidemiological designs (cross-sectional, longitudinal clinical trial or cohort study, case-control study).
Determine the power of statistical tests for a given sample size and minimal detectable difference in the context of epidemiological study designs
Use modern computational and graphical tools in assessing power and sample size

Methods of Assessment:

There are 6 assignments with each assignment having 4 questions. Students have to pass Assignment 1-5 (achieving at least 3 correct answers in each assignment) in order to pass the class. Students who fail an assignment will be offered the opportunity to retake it within a timeline.

Prerequisite:

Introduction to Online Learning (a free mini course) is required prior to participating in any of the School's Internet-based courses. Either 340.601 or 340.751, and prior enrollment in 140.622, 550.695, or equivalent, is required. Prior knowledge of some computing software (R, Stata, and/or SAS) is required for students taking the course for credit. Prior exposure to some sample size software (PS, PASS, nQuery Advisor or Epi Info) is helpful for non-credit students.

Grading Restriction: Pass/Fail

Special Comments: Students need to listen to lectures and complete 6 online quizzes until they successfully master the material.