140.654.01
Methods in Biostatistics IV
Location
East Baltimore
Term
4th Term
Department
Biostatistics
Credit(s)
4
Academic Year
2013 - 2014
Instruction Method
TBD
Tu, Th, 10:30 - 11:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
140.651-653
Focuses on regression analysis for continuous and discrete data, 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:
- Formulate a scientific question about the relationship of a response variable Y and predictor variables X in terms of the appropriate logistic, log-linear or survival regression model
- Interpret the meaning of regression coefficients in scientific terms as if for a substantive journal 2.1 For binary responses collected in clusters, distinguish between marginal and cluster-specific regression coefficients estimated by ordinary and condit
- Develop graphical and/or tabular displays of the data to show the evidence relevant to describing the relationship of Y with X (3.1 For survival data, produce Kaplan-Meier and complimentary log, log plots of survival functions with standard errors)
- Estimate the model using a modern statistical package such as STATA or R and interpret the results for substantive colleagues 4.1 Derive the estimating equations for the maximum likelihood estimates for the class of generalized linear models and state the
- Give a heuristic derivation of the Cox proportional hazards estimating function in terms of Poisson regression for grouped survival data
- Check the major assumptions of the model including independence and model form (mean, variance and distribution of residuals, proportional hazards) and make changes to the model or method of estimation and inference to appropriately handle violations
- Use weighted least squares for situations with unequal variances
- Use robust variance estimates for violations of independence or variance or distributional assumptions
- Use stratification of follow-up time to deal with non-proportional hazards
- 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 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