summary of the effects after logistic regression model

Dear all, my aim is to estimate the efficacy over time  of a treatment for
headache prevention.  Data consist of long sequences of repeated binary
outcomes   (1 if the subject has at least 1 episode of headache  , 0
otherwise) on subjects randomized to placebo or treatment.

I have fit a logistic regression model with Huber-White cluster sandwich
covariance estimator.
I have put in the model  the variables treatment (trt),sex,age and a
restricted cubic spline of time (days)  to allow for non-linear treatment
effects.

I use the functions lrm and robcov from R Design library:

h<-lrm(head ~ trt*rcs(days)+ age+ sex,x=T,y=T)
h.rob<-robcov(h,id)

I want to estimate treatment effect over time, then:

k<-contrast(h.rob,list(day=1:240, trt=1),
                          list(day=1:240, trt=0))

xYplot(Cbind(exp(Contrast), exp(Lower),exp( Upper)) ~ day, data=k)   #Plot
of treatment effects (odds ratio).

The treatment group has a disavantage at the baseline ( for day=1 ,OR=1.16),
however at day=210 I can see a reduction of headache risk (OR=0.58) on
treatment group.

How can I set to 1  the OR of treatment at the baseline (day=1) with R? In
case, is it corrent?

Best regards

Andrea Evangelista
Italy

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