1. Inthestudydescribedinproblem 1,theinvestigatorssummarizedthedatainthefollowing2x2tablebasedonwhethertheCD4countdroppedbelow200bytheendofthestudy.Thefollowingtableshowstheirobserveddata:

	CD4<200atendofstudy	CD4>=200atendofstudy	Total
ARV1	50	190	240
ARV2	90	170	260
Total	140	360	500

Continuing on the analysis from question 2, you decide to adjust for other variables in your analysis. For your dependent (Y) variable, you code it 0 for each person where CD4 is below 200 at the end of study and 1 if CD4 is 200 or above. Then you build a model predicting a function of Y based on age, gender, time since infection, and ARV group in predicting the (log) odds that CD4 count is above 200 by the end of the study.

a. What is the name for this type of model

Many of the patients were very sick, and quite a few died from AIDS-associated opportunistic infections or TB before one year. Since the CD4 count at one year was unavailable for these patients, one of the investigators proposed changing the analysis to compare the time to CD4 count < 200, and censoring the patients who died or reached the end of the study without falling below 200.

b. What important assumption does this idea seem to violate?

After hearing your explanation, the investigator agrees to change the endpoint to time to event, where an event is any of: CD4<200, AIDS defining illness, or death.

c. What kind of test could she use to compare the time to event in the two treatment groups?