I have a survival dataset which I’ve experimented with to create several Cox PH models using different techniques (lasso, forward selection, backwards elimination etc), however no matter which technique I use, I can’t get a concordance index above 0.57.
The dataset consists of a little over 12,000 rows with 88 variables relating to lung transplantation. The time-to-event is time until death after transplant which is right-censored.
Here is an example model and concordance calculation:
res.cox <- coxph(Surv(pdata$ptime, pdata$death_cens) ~ rcs(tx_age) + rcs(func_stat_tx) + rcs(egfr), pdata, iter.max=100)
Call: concordance.coxph(object = res.cox) n= 12335 Concordance= 0.571 se= 0.004397 concordant discordant tied.x tied.y tied.xy 20587197 15465375 5400 7011 0
Is there a way of visualising/analysing the data to determine the cause of the poor concordance index?