DATA: subjid age sex bmi race wk1derm wk1dermthres wk1global wk1globalthres wk1skin wk1skinthres wk8derm wk8dermthres wk8global wk8globalthres wk8skin sk8skinthres wk10derm wk10dermthres wk10global wk10gobalthres wk10skin wk10skinthres 1 65 1 24.9 1 5 0 1 1 9 0 0 1 0 1 7 1 1 1 0 1 5 1 2 45 1 23.9 2 6 0 0 1 8 0 1 1 0 1 7 1 0 1 0 1 5 1 3 70 0 27 2 6 0 2 0 0 1 3 0 1 1 2 1 0 1 1 1 0 1 4 48 1 26 3 10 0 2 0 7 1 9 0 2 0 6 0 8 0 2 0 7 1 5 36 0 15.5 5 15 0 5 0 12 0 12 0 4 0 10 0 12 0 4 0 10 0 I would like to run logistic regression models for questionnaires given to patients at each week. Each questionnaire is a continuous variable but also has a responder threshold: DERM questionnaire threshold: score=0/1 vs >1 Global questionnaire threshold: score=0/1 vs >1 SKIN questionnaire threshold: score= <8 vs >=8 I want to run regression models to assess the impact of potentially relevant covariates where the SKIN threshold is the independent variable and the other two (DERM and global thresholds) are the dependent variables. I also want to find out what other covariates (e.g., age [continuous], sex, bmi [continuous], race) should be included as predictors in the models. i have attempted doing the codes for week1 below and wanted to verify if they look correct: proc logistic data = "c:mydatahsb2" desc;
model wk1DERMthres = wk1SKINthres AGE sex RACE BMI / expb;
run; proc logistic data = "c:mydatahsb2" desc;
model wk1Globalthres = wk1SKINthres AGE sex RACE BMI / expb;
run; if i want to conduct ANCOVA models for the questionnaires (DERM, Global) as continuous variables (total scores) to evaluate the association between DERM and SKIN threshold and Global and SKIN threshold and to assess whether or not other variables (e.g., age, sex, race, bmi) have an impact on the association, are these the correct codes: proc logistic data=work.question; class wk1SKINthres; model wk1DERM= wk1SKINthres age wk1SKINthres*age/solution; lsmeans wk1SKINthres*age/tukey line; run; proc glm data=work.question; class wk1SKINthres; model wk1Global = wk1SKINthres age wk1SKINthres*age/solution; lsmeans wk1SKINthres*age/tukey line; run; proc glm data=work.question; class wk1SKINthres race; model wk1DERM= wk1SKINthres race wk1SKINthres*race/solution; lsmeans wk1SKINthres*race/tukey line; run; proc glm data=work.question; class wk1SKINthres race ; model wk1Global = wk1SKINthres race wk1SKINthres*race/solution; lsmeans wk1SKINthres*race/tukey line; run;
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