140.614.11 | AY 2013-2014 - Summer Inst. Term | 2 Credit(s)
MTWThF 1:30:00 PM
  • Contact Information
    John McGready
  • Course 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 continous 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 continous 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
    • Set up 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
  • Course 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 11.0 installed.
  • Intended Audience
    JHSPH students and SI participants
  • Methods of Assessment
    Student evaluation based on laboratory exercises, an exam, and completion of an independent data analysis project.
  • Prerequisites
  • Course Schedule

    Please see the course Session for a full list of dates and items for this course.

  • Academic Ethics Code

    Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.

  • Disability Support Services
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