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140.614.11
Data Analysis Workshop II

Location:
Online/Virtual
Term:
Summer Inst. term
Department:
Biostatistics
Credits:
2 credits
Academic Year:
2021 - 2022
Instruction Method:
Synchronous Online
Dates:
Mon 06/21/2021 - Fri 06/25/2021
Class Times:
  • M Tu W Th F,  1:30 - 5:00pm
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Ayesha Khan
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 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:

This course is evaluated as follows:

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

Enrollment Restriction:

0

Instructor Consent:

No consent required

Special Comments:

Students must have a laptop/computer with Stata installed. Course will be taught online, via Zoom, on the scheduled dates and times. All in-person classes will be taught online via Zoom, on the dates and times the course is scheduled. For further information, please see the Institute website jhsph.edu/summerepi