140.653.01
Methods in Biostatistics III
 Location:
 East Baltimore
 Term:
 3rd term
 Department:
 Biostatistics
 Credits:
 4 credits
 Academic Year:
 2022  2023
 Instruction Method:
 Inperson
 Class Times:

 Tu Th, 10:30  11:50am
 Lab Times:


Tuesday, 3:30  4:20pm

 Auditors Allowed:
 Yes, with instructor consent
 Undergrads Allowed:
 No
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructor:
 Contact:
 Elizabeth Johnson
 Resources:
 Prerequisite:
 Description:

Focuses on regression analysis for continuous and discrete responses, and data analyses that integrate the methods learned in 140.651652. Regression topics include simple linear regression; a matrix formulation of multiple linear regression; inference for coefficients, predicted values, and residuals; tests of hypotheses; graphical displays and regression diagnostics; specific models, including polynomial regression, splines, one and twoway ANOVA; variable selection; nonparametric regression; loglinear models for incidence rates and contingency tables; logistic regression; and generalized linear models.
 Learning Objectives:

Upon successfully completing this course, students will be able to:
 Formulate a scientific question about the relationship of a continuous response variable Y and predictor variables X in terms of the appropriate linear regression model. Use indicator variables, linear and cubic regression splines, and interaction terms to represent major scientific questions in terms of a linear regression model
 Interpret the meaning of regression coefficients in scientific terms as if for a substantive journal. Explicitly define the epidemiologic terms “confounding” and “effect modification” in terms of multiple regression coefficients
 Develop graphical and/or tabular displays of the data to display the evidence relevant to describing the relationship of Y with one X controlling for others. Use an adjusted variables plot to explain the meaning of a multiple regression coefficient.
 Estimate the model using a modern statistical package such as STATA or R and interpret the results for substantive colleagues. Derive the least squares estimators for the linear model and the distribution of coefficients, predicted values, residuals and linear functions of them.
 Check the major assumptions of the model including independence and model form (mean, variance and distribution of residuals) and make changes to the model or method of estimation and inference to appropriately handle violations of standard assumptions. Use weighted least squares for situations with unequal variances. Use robust variance estimates for violations of independence or variance or distributional assumptions. Use regression diagnostics to prevent a small fraction of observations from having undue influence on the results
 Write a methods and results section for a substantive journal, correctly describing the regression model in scientific terms and the method used to specify and estimate the model. Correctly interpret the regression results to answer the specific substantive questions posed in scientific terms that can be understood by substantive experts
 Critique the methods and results from the perspective of the statistical methods chosen and alternative approaches that might have been
 Methods of Assessment:
This course is evaluated as follows:
 10% Participation
 30% Problem sets
 30% Quizzes
 30% Final problem set/project
 Instructor Consent:
No consent required
 Special Comments:
Please note: This is the inperson section of a course that is also offered virtually/online. Students will need to commit to the modality for which they register.