Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model
fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to treatment in the trial, but Covariate is something that is measured posthoc and can vary in the population. I am trying to wrap my head around how to calculate a few quantities from the model and get reasonable confidence intervals for them, namely I would like to test H0: gamma=0, where gamma is the regression coefficient of the odds ratios of surviving under treatment vs control at each level of Covariate (adjusted for the confounder) and I would like to get the odds of surviving at each level of Covariate under treatment and control for each level of covariate adjusted for the confounder. I have looked at contrast in the Design library but I don't think it gives me the right quantity, for instance contrast(fit,list(covariate="A", Therapy="Treated", confounder=median(myDat$confounder), X=TRUE) ( "A" is the baseline level of Covariate) gives me beta0 + beta_Treated + beta_confounder*68 Is this correctly interpreted as the conditional odds of dying? As to the 1st contrast I am not sure how to get it, would it be using type = 'average' with some weights in contrast? The answers are probably staring me in the face, i am just not seeing them today. Nicholas ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.