It is very uncommon for the assumptions underlying this method to be satisfied. These assumptions include (1) the relationship between X and log relative hazard is discontinuous at X=c and only X=c; (2) c is correctly found as the cutpoint; (3) X vs log hazard is flat to the left of c; (4) X vs log hazard is flat to the right of c; (5) the 'optimal' cutpoint does not depend on the values of other predictors.
These relationships rarely occur in nature unless X=time. Failure to have these assumptions satisfied will result in (1) great error in estimating c (because c doesn't exist); (2) low predictive accuracy; (3) serious lack of fit; (4) residual confounding; and (5) overestimation of effects of remaining variables. This non-existence of cutpoints is why in medical research no two investigators seem to find the same cutpoint for the same predictor in different datasets. Frank ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Function-comparable-to-cutpt-coxph-from-Survival-Analysis-using-S-tp3229420p3229704.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
