140.614.73
Data Analysis Workshop II
- Location:
- Internet
- Term:
- Winter Inst. term
- Department:
- Biostatistics
- Credits:
- 2 credits
- Academic Year:
- 2022 - 2023
- Instruction Method:
- Synchronous Online
- Dates:
- Fri 01/13/2023 - Fri 01/20/2023
- Class Times:
-
- Tu W Th F, 1:30 - 4:50pm
- Auditors Allowed:
- Yes, with instructor consent
- Undergrads Allowed:
- Yes
- Grading Restriction:
- Letter Grade or Pass/Fail
- Course Instructor:
-
- Jonathan Gellar
- Contact:
- Jonathan Gellar
- Resources:
- Prerequisite:
- 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.
- Learning Objectives:
-
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:
This course is evaluated as follows:
- 80% Lab Assignments
- 20% Final Exam
- Instructor Consent:
Consent required for some students
- Consent Note:
Instructor consent required for registrants not enrolled in a degree program currently with enrollment in the Winter Institute.
- For consent, contact:
- Special Comments:
Instructor consent required for registrants not concurrently enrolled in a JHSPH part-time degree program. Enrollment limited: students must have a laptop computer with Stata/IC versions 13.0, 14.0, or 15.0 installed.