Data Analysis Workshop II
June 17-21, 2024
1:30 p.m. – 5:00 p.m.
2 credits
Course Number: 140.614.49 (synchronous online)
140.614.11 (in person)
This is a hybrid course with both a synchronous online section (140.614.49) and an in-person section (140.614.11). You'll be able to indicate which section you want (either in-person or online) when registering in SIS.
Course Instructor:
Description:
Intended for students with a broad understanding of biostatistical concepts used in public health sciences who seek to develop additional data analysis skills. Emphasizes concepts and illustration of concepts applying a variety of analytic techniques to public health datasets in a computer laboratory using Stata statistical software. Masters advanced methods of data analysis including analysis of variance, analysis of covariance, nonparametric methods for comparing groups, multiple linear regression, logistic regression, log-linear regression, and survival analysis.
Student Evaluation: Student evaluation based on laboratory exercises, an exam, and completion of an independent data analysis project.
Learning Objective:
Upon successfully completing this course, students will be able to:
- Use STATA to visualize relationships between two continuous measures
- Use STATA to fit simple linear regression models, and interpret relevant estimates from the results
- Use STATA to fit multiple linear regression models to relate a continuous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
- Interpret the relevant estimates from multiple linear regression
- Use STATA to graph lowess smoothing functions to relate the probability of a dichotomous outcome to a continuous predictor
- Use STATA to fit multiple logistic regression models to relate a dichotomous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
- Setup cohort study data into STATA survival analysis format
- Use STATA to graph Kaplan-Meier curves and perform log-rank tests
- Use STATA to fit Cox regression models to relate time-to-event data to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
- Interpret the confounding estimates from Cox regression
Methods of Assessment:
1) Lab Assignments and Quizzes 60%
2) Final Project 40%
Location: Baltimore
Prerequisite: 140.611 and 140.612 or equivalent
Grading Options: Letter Grade or Pass/Fail
Course Materials: Students must have a laptop computer with Intercooled Stata 17 or Intercooled 16 installed. Student discounts are available for Intercooled Stata.
Related Courses: Data Analysis Workshop I • Advanced Data Analysis Workshop