140.653.41Methods in Biostatistics III

Location:
Internet
Term:
3rd term
Department:
Biostatistics
Credits:
4 credits
2022 - 2023
Instruction Method:
Synchronous Online
Class Times:
• Tu Th,  10:30 - 11:50am
Lab Times:
• Tuesday,  3:30 - 4:20pm
Auditors Allowed:
No
No
Course Instructor:
Contact:
Elizabeth Colantuoni
Resources:
Prerequisite:

140.652

Description:

Focuses on regression analysis for continuous and discrete responses, and data analyses that integrate the methods learned in 140.651-652. 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 two-way ANOVA; variable selection; non-parametric regression; log-linear 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:

1. 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
2. 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
3. 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.
4. 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.
5. 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
6. 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
7. 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