Hi, Sorry to take so long to reply, I was travelling last week. Thanks for your suggestions. Actually in this case contrast and predict gave the same result, and what I was looking at was the correct odds from the model.
What is still confusing me is the 1st part of my question, looking for a trend in odds ratios. From what I understand testing the interaction: fit1<-glmD(survived ~ as.numeric(Covariate)+Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) fit2<-glmD(survived ~ as.numeric(Covariate)*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) lrtest(fit1,fit2) Would be effectively testing for a trend in odds ratios? Do I have to fiddle with contrasts to make sure I am testing the correct parameter? Thanks Nicholas On Sat, 16 Jun 2007 11:14:12 -0500, "Frank E Harrell Jr" <[EMAIL PROTECTED]> said: > Nicholas Lewin-Koh wrote: > > 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()) > > For logistic regression you can also use Design's lrm function which > gives you more options. > > > > > 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. > > If by posthoc you mean that the covariate is measured after baseline, it > is difficult to get an interpretable analysis. > > > > > 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) > > You mean regression coefficient on the log odds ratio scale. This is > easy to do with the contrast( ) function in Design. Do ?contrast.Design > for details and examples. > > > > > 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. > > contrast( ) is for contrasts (differences). Sounds like you want > predicted values. Do ?predict ?predict.lrm ?predict.Design. Also do > ?gendata which will generate a data frame for getting predictors, with > unspecified predictors set to reference values such as medians. > > Frank > > > > > Nicholas > > > > > > > > > -- > Frank E Harrell Jr Professor and Chair School of Medicine > Department of Biostatistics Vanderbilt University ______________________________________________ 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.