[R] complex contrasts and logistic regression

[Nicholas Lewin-Koh]

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"
<f.harrell at vanderbilt.edu> 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