Objective: Upon completion of this course learners will be better able to solve problems in epidemiologic research regarding the planning, analysis, and interpretation of complex studies.
Description: This course covers state-of-the-science epidemiologic methods. We concentrate on methods based on sound theoretical principles which maximize the accuracy of inferences drawn from analyses of complex randomized and nonrandomized (observational) studies. We discuss a series of topics using assigned readings and programs written in SAS and R. Students will work in groups on a course project.
Lecture code in SAS and R + output (in html file format made in R markdown file, .Rmd)
Lecture 3: G-computation with NAMCS data -- .Rmd file -- .sas file
Lecture 4: Propensity Score and Inverse-Probability of Treatment Weighted Estimators -- .Rmd file -- .sas file
Lecture 5: Bootstrap Estimation -- .Rmd file -- .sas file
Lecture 6: MLE -- .Rmd file -- .sas program 1 -- .sas program 2 -- .sas program 3
Lecture 7: Bayes -- .Rmd file -- .sas program 4
Lecture 8: Survival -- .Rmd file -- .sas program 5
Lecture 9: IP-weighted survival -- .Rmd file -- .sas program 6
Lecture 10: IP-weighted Cox models -- .Rmd file -- .sas program 7
Lecture 11: Generalizability -- .Rmd file -- .sas program 8
Lecture 12: Missing -- .Rmd file -- .sas program 9
Project Summary slides (html) -- Project Summary slides (pdf) -- .Rmd file for slides -- .Rmd file for analyses in slides
Recitation
Day 1 (2016-01-25): R intro sample code -- .Rmd file producing code -- .sas file with SAS code
Day 2 (2016-02-01): g-comp R and SAS review -- .Rmd file producing code -- .sas
Day 3 (2016-02-08): IPTW R and SAS review -- .Rmd file -- .sas -- .R
NOTE: To print off the following ioslides for Day 5, use Google Chrome and print to pdf. Otherwise it won't work.
Day 5 (2016-02-29): Cox PH SAS and R review -- .Rmd file
[Lecture review] (http://unc-epid-722.github.io/2016/review.html) -- .Rmd file
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