140.654.01
Methods in Biostatistics IV
 Location:
 East Baltimore
 Term:
 4th term
 Department:
 Biostatistics
 Credits:
 4 credits
 Academic Year:
 2012  2013
 Instruction Method:
 TBD
 Class Times:

 Tu Th, 10:30  11:50am
 Auditors Allowed:
 Yes, with instructor consent
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructor:
 Contact:
 Hong Kai Ji
 Resources:
 Prerequisite:
140.651653
 Description:

Focuses on regression analysis for continuous and discrete data, 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 response variable Y and predictor variables X in terms of the appropriate logistic, loglinear 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 clusterspecific regression coefficients estimated by ordinary and conditional logistic regression
 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 KaplanMeier 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 asymptotic distributions of the regression coefficients and linear combinations thereof; 4.2 Give a heuristic derivation of the Cox proportional hazards estimating function in terms of Poisson regression for grouped survival data
 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 followup time to deal with nonproportional 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
 Methods of Assessment:
Method of student evaluation based on problem sets, an exam, and a data analysis project.
 Instructor Consent:
No consent required