Last Update: January 9, 2004.
Time & Location: MWF 9:00-9:50 pm, in Keck Hall room 107
Instructor: Kenneth R.
Office: MD Anderson Faculty Center, FC2.3030
Woodward M. Epidemiology: Study Design and Data Analysis. Boca Raton:
Chapman & Hall / CRC, 1999.
Supplemental Texts: Venables WN, Ripley BD. Modern Applied Statistics with S. 4th ed. Springer, 2002; B Everitt, S Rabe-Hesketh, Analyzing Medical Data Using S-Plus. Springer, 2001
Course Description: This course covers the design of biomedical and epidemiological studies and the analysis of the resulting data. Because this is primarily a course for statistics majors, the applied methods will be related to theory whenever practical. Emphasis will be placed on the similarity between various forms of analysis and reporting results in terms of measures of effect or association. Emphasis will also be given to identifying statistical assumptions and performing analyses to verify these assumptions. S-Plus (R) will serve as the basic computing software.
Course Prerequisites: STAT 410
Computing: Weekly homework will involve statistical analyses that will often require access to sophisticated statistical software. In general, students may use the software of their choice. However, a few assignments may require S-PLUS, and computing instructions, when given, will be in S-PLUS.
Assignments: Weekly homework will be a mix of data analysis, computer simulations, report writing, and answering questions relating to relevant statistical theory and methods. Students are encouraged to seek help from the TA, theinstructor or other students as to the methods of solution, but the submitted report should reflect only the students' work. Reports will be written legibly (or typed), and be grammatically correct.
60 % Homework
30 % Final
10 % Oral Participation
1 Overview, study design, data, graphs, inference
2 Comparing means (t-tests, ANOVA, rank-based)
3 Simulations, permutation tests, bootstrapping,
Comparing proportions (binomial, chi-square test)
4 Survival analysis (censoring, hazard function) 5 Regression analysis (linear, logistic, residuals)
6 Proportional hazard regression analysis
7 Rates and counts (standardization, Poisson regression)
8 Multivariable analysis (stratification, regression)
9 Model assessment (goodness of fit, predictive accuracy)
10 Biomarkers, replication, multilevel data (pairing)
11 Longitudinal data, multilevel models, multistate models
12 Supervised and unsupervised data mining
13 Microarray data analysis, diagnostic tests
14 Power and Sample Size, Clinical trials
Any student with a disability requiring accommodations in this course is encouraged to contact the professor after class or during office hours. Additionally, students should contact Disability Support Services in the Ley Student Center.
Send problems or suggestions to firstname.lastname@example.org