Part 1:
Write a proposal for a study using observational data that requires the use of propensity score analysis. Have two treatments defined by behaviors/actions of people. Keep in mind what covariates that explain or are related to that choice. These covariates are potential confounders. Other things to keep in mind: How long is the follow up in the study? What response variables will you choose to assess the differences between the treatments? Note that some covariates can be obtained with lab results (ie, baseline cholesterol levels in a study involving a cardiovascular disease)
Part 2:
Do an analysis with the "caith" data set (can be found in the package MASS). Packages vcd, ca, and MASS in R are all good options for the analysis.
(Part 2 can be expanded on)
Comments (2)
bob pruzek said
at 5:01 pm on Apr 2, 2011
Let me just add a small point to Part 1, which conveys well what I asked for: Try to finish Part I by late this weekend
so you can start reading the new post items that deal w/ correspondence analysis and mosaics (as well as doing
the analysis of -- at least -- the caith data). The latter could easily take 3-4 hours of your time. Thanks, Matthew. Bob
bob pruzek said
at 10:26 am on Apr 3, 2011
You all may appreciate the functions assocplot and mosaicplot (which should be available in your R environment because they are loaded by default). Check them out! bp
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