Hi,

I am analysing survival data (diagnosis time until death/cens) with time-dependent

 covariates.  I would like to fit a cox model using the (start, stop] variable.

   

  In summary, I have the multiple internal time dependent covariates as follows;



1). LAS score (measured weekly on low risk patients, monthly on high risk)


2). EORTC score (measured monthly on low risk patients and every 3 months on

 high risk)
3). BMI (measured monthly on low risk patients and every 3 months on high risk)



I have referred to the John Fox 'Cox Proportional-Hazards Regression for Survival

 Data' 
http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf

 
and the corresponding script file at 
http://cran.r-project.org/doc/contrib/Fox-Companion/cox-regression.txt

and also to Therneau and Grambsch.

My problem is creating the dataset, possibly using the fold function (as described

 in Fox, p9) with more than one time-dependent covariate  (which I successfully

 did with LAS).  I have longitudinal measurements for each subject (with each

 date of assessment) as above with some missing data in a period of time before

 death (which I have entered as NA).

Since the measurements in time depend on whether the patient is high or low

 risk and are made at different time intervals for each covariate, I wasn't

 sure how to code this in R.    

I would be really grateful if anyone could start me off on this.

Thank you to those who respond.

Zoe

