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 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
