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140.614.95
Data Analysis Workshop II

Location:
Kyoto, Japan
Term:
4th term
Department:
Biostatistics
Credits:
2 credits
Academic Year:
2022 - 2023
Instruction Method:
In-person
Class Times:
  • Wednesday,  1:00 - 5:00pm
  • Th F,  8:30am - 5:00pm
Auditors Allowed:
No
Undergrads Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Marie Diener-West
Resources:
Prerequisite:

140.613

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:

  1. Use STATA to visualize relationships between two continuous measures
  2. Use STATA to fit simple linear regression models, and interpret relevant estimates from the results
  3. 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
  4. Interpret the relevant estimates from multiple linear regression
  5. Use STATA to graph lowess smoothing functions to relate the probability of a dichotomous outcome to a continuous predictor
  6. 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
  7. Setup cohort study data into STATA survival analysis format
  8. Use STATA to graph Kaplan-Meier curves and perform log-rank tests
  9. 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
  10. Interpret the confounding estimates from Cox regression
Methods of Assessment:

This course is evaluated as follows:

  • 60% Lab Assignments and Quizzes
  • 40% Final Project

Enrollment Restriction:

Enrollment restricted to students in the Kyoto MPH cohorts

Instructor Consent:

No consent required

Special Comments:

Students must have a laptop computer with Stata installed. Course meeting times are: Wednesday, March 23, 1-5; and Thursday, March 24, and Friday, March 25, 8:30-5