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Course Catalog

140.614.20 Data Analysis Workshop II

Department:
Biostatistics
Term:
3rd term
Credits:
2 credits
Academic Year:
2017 - 2018
Location:
East Baltimore
Dates:
Thu 02/22/2018 - Fri 02/23/2018
Class Times:
  • Th F,  8:30am - 5:00pm
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Judith Holzer
Course Instructor:
  • Xiangrong Kong
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. In the second workshop (140.614), students will master 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. Enrollment limited: students must have a laptop computer with Stata/IC versions 13.0, 14.0, or 15.0 installed.

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:

Student evaluation based on laboratory exercises, an exam, and completion of an independent data analysis project.

Enrollment Restriction:

Part-time DrPH students in the Tsinghua cohort only

Instructor Consent:

Consent required for all students

For consent, contact:

judith.holzer@jhu.edu

Special Comments:

This course will be offered over a 2-day period in Baltimore. Students may be required to complete assignments prior to the start of class.