Dear Thomas & Dear List, the fitting function `coxph.fit' called by `coxph' may fail to estimate the regression coefficients when some values of the design matrix are very large. For example library(survival) ### load example data load(url("http://www.imbe.med.uni-erlangen.de/~hothorn/coxph_fit.Rda")) method <- "efron" ### copied from `coxph.fit' coxfit <- .C("coxfit2", iter=as.integer(maxiter), as.integer(n), as.integer(nvar), stime, sstat, x= x[sorted,] , as.double(offset[sorted] - mean(offset)), as.double(weights), newstrat, means= double(nvar), coef= as.double(init), u = double(nvar), imat= double(nvar*nvar), loglik=double(2), flag=integer(1), double(2*n + 2*nvar*nvar + 3*nvar), as.double(control$eps), as.double(control$toler.chol), sctest=as.double(method=="efron") ,PACKAGE="survival") produces R> coxfit$coef [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN because (?) R> summary(x[,1]) Min. 1st Qu. Median Mean 3rd Qu. Max. 2.378e-01 8.758e+00 5.872e+01 1.640e+04 2.732e+02 4.000e+06 One the other hand x[,1] <- x[,1]/max(x[,1]) coxfit <- .C("coxfit2", iter=as.integer(maxiter), as.integer(n), as.integer(nvar), stime, sstat, x= x[sorted,] , as.double(offset[sorted] - mean(offset)), as.double(weights), newstrat, means= double(nvar), coef= as.double(init), u = double(nvar), imat= double(nvar*nvar), loglik=double(2), flag=integer(1), double(2*n + 2*nvar*nvar + 3*nvar), as.double(control$eps), as.double(control$toler.chol), sctest=as.double(method=="efron") ,PACKAGE="survival") looks much better R> coxfit$coef [1] 0.25123203 0.00000000 -0.42595541 -0.04488913 -0.26061995 0.44426458 [7] -0.38954286 -0.43081374 0.79573107 0.48234405 0.94636357 -0.25193465 [13] 1.15619712 0.32651765 -1.06731019 -0.24249939 I nailed the problem down to lines 261ff of `coxfit2.c' where zbeta = offset[person]; for (i=0; i<nvar; i++) zbeta += newbeta[i]*covar[i][person]; risk = exp(zbeta ) * weights[person]; and `zbeta' may become very large and thus `exp' returns `nan'. Of course one could transform the independent variables prior to fitting but I'm not sure if this is the intented solution. Best, Torsten