[R] Fitting a kind of Proportional Odds Modell using nlme, polr, lrm or ordgee
Henning Henke
henke at statistik.uni-dortmund.de
Wed Apr 27 17:12:35 CEST 2005
Hello,
I'm trying to fit a special kind of proportional odds model from:
Whitehead et al. (2001). Meta-analysis of ordinal outcome using
individual patient data. Statistics in medicine 20: 2243-2260. (model 2)
The data are as follows:
library(nlme)
library(geepack)
library(Design)
library(MASS)
options(contrasts=c("contr.SAS","contr.poly"))
counts <-
c(2,22,54,29,3,4,23,45,22,2,1,22,35,11,3,14,119,180,54,6,7,16,17,
10,3,13,20,24,10,1,8,24,73,52,13,21,106,175,62,17,2,13,18,7,1,3,14,19,3,0)
category <- c(rep(1:5,10))
treatment <- (rep(c(0,0,0,0,0,1,1,1,1,1,),5))
study <- gl(5,10)
percoftotal <- counts/sum(counts)
cout <- data.frame(study,treatment,category,counts,percoftotal)
The data are from five controlled clinical trials (study). The patients
were given placebo (treatment=0) or treatment (treatment=1) and had a
response in one of five categories.
Now I want to fit the model given by Whitehead et al. This assumes
proportional odds between treatments, but stratifies by study. This
means that the cut-off points associated with the distribution of the
underlying latent variable for determining the response category are
allowed to vary from study to study but are the same for both treatment
groups within a study.
It is given by
log(Q_{ijk}/(1-Q_{ijk}) = \alpha_{ik} + \beta*x_{ij} (k=1,...,m-1)
This model can be considered as arising from a latent continuos variable
. Assume that the response of the j-th subject in study i is truly equal
to G_{ij} although this latent reponse will never be observerd.
G_{ij} has a logistic distribution with:
Q_{ijk} = P(G_{ij} <= \alpha_{ik}) =
1/(1+exp(-(\alpha_{ik}+\beta*x_{ij}) (k=1,...,m-1)
in which Q_{ijk} is the probabilty of having a response for the j-th
subject in category k or better that means p_{ij1}+....+p_{ijk}= Q_{ijk}
and Q_{ijm}=1, Q_{ij1}=1.
i = 1,...,r (r=5) #number of studies
j = 1,....,n_{i} #subject j from study i
k = 1,...,m (m=5)#Category
My problem is how to fit this model in R although I have a SAS code from
Whitehead which is availabe at
http://www.rdg.ac.uk/mps/mps_home/misc/publications.htm . There it is
fit by using Proc NLMIXED. The result for beta is there:
The problem is that alpha_{ik} represents the k_th intercept for the
i_th study. A "normal" Proportional-Odds model
has only alpha_k which represents the k-th intercept without any
associaton to the study.
I tried to fit it by using the functions of Pinheiro/ Bates nlme, nls,
gnls;
polr by Venables/Ripley ;
lrm by Harrell
ordgee by Yan;
but I came to no conclusion.
I would be very happy if anyone could help me.
Thank you VERY VERY much in advance.
Kindly regards,
Henning Henke
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