[R-sig-ME] [R] some questions about longitudinal study with baseline

[Frank Harrell]

Baseline should appear only as a baseline and should be removed from 
the set of longitudinal responses.  This is often done with a merge( ) 
operation.

Frank

Frank E Harrell Jr   Professor and Chairman        School of Medicine
                      Department of Biostatistics   Vanderbilt University

On Tue, 7 Sep 2010, array chip wrote:

> Hi all,
>
> I asked this before the holiday, didn't get any response. So would like to
> resend the message, hope to get any fresh attention. Since this is not purely
> lme technical question, so I also cc-ed R general mailing list, hope to get some
> suggestions from there as well.
>
>
> I asked some questions on how to analyze longitudinal study with only 2 time
> points (baseline and a follow-up) previously. I appreciate many useful comments
> from some members, especially Dennis Murphy and Marc Schwartz who refered the
> following paper addressing specifically this type of study with only 2 time
> points:
>
> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1121605/
>
> Basically, with only 2 time points (baseline and one follow-up), ANCOVA with
> follow-up as dependent variable and baseline as covariate should be used:
>
> follow-up = a + b*baseline + treatment
>
> Now I have a regular longitudinal study with 6 time points, 7 treatments
> (vehicle, A, B, C, D, F, G), measuring a response variable "y". The dataset is
> attached. I have some questions, and appreciate any suggestions on how to
> analyze the dataset.
>
> dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL)
> library(MASS)
> dat$trt<-relevel(dat$trt,'vehicle')
>
> xyplot(y~time, groups=trt, data=dat,
> ylim=c(3,10),col=c(1:6,8),lwd=2,type=c('g','a'),xlab='Days',ylab="response",
> key = list(lines=list(col=c(1:6,8),lty=1,lwd=2),
>                  text = list(lab = levels(dat$trt)),
>                  columns = 3, title = "Treatment"))
>
> So as you can see that there is some curvature between glucose level and time,
> so a quadratic fit might be needed.
>
>
>
> dat$time2<-dat$time*dat$time
>
> A straight fit like below seems reasonable:
>
> fit<-lmer(y~trt*time+trt*time2+(time|id),dat)
>
> Checking on random effects, it appears that variance component for random slope
> is very small, so a simpler model with random intercept only may be sufficient:
>
> fit<-lmer(y~trt*time+trt*time2+(1|id),dat)
>
> Now, I want to incorporate baseline response into the model in order to account
> for any baseline imbalance. I need to generate a new variable "baseline" based
> on glucose levels at time=0:
>
> dat<-merge(dat, dat[dat$time==0,c('id','y')], by.x='id',by.y='id',all.x=T)
> colnames(dat)[c(4,6)]<-c('y','baseline')
>
> so the new fit adding baseline into the mixed model is:
>
> fit<-lmer(y~baseline+trt*time+trt*time2+(1|id),dat)
>
> Now my question is 1). Is the above model a reasonable thing to do? 2) when
> baseline is included as a covariate, should I remove the data points at baseline
> from the dataset? I am kind of unsure if it's reasonable to use the baseline
> both as a covariate and as part of the dependent variable values.
>
> Next thing I want to do with this dataset is to do multiple comparisons between
> each treatment (A, B, C, D, F, G) vs. vehicle at a given time point, say time=56
> (the last time points) after adjusting the baseline imbalance. This seems to be
> done using Dunnet test. When I say "after adjusting baseline imbalance", I mean
> the comparisons should be done based on the difference between time=56 and
> time=0 (baseline), i.e. is there any difference in the change from baseline for
> treatment A (or B, C, D, F, G) vs. vehicle?. How can we test this? Will glht()
> in multcomp work for a lmer fit? If yes, how can I specify the syntax?
>
> Finally, with the above model, how to estimate the difference (and the standard
> error) between time=56 and time=0 (baseline) for each treatment groups?
>
> Thank you all for your attention.
>
> John
>
>
>