Problem in cph, when there is missing value and cluster

[tamas-ferenci]

Here is a minimal reproducible example:

set.seed(1)

RawData <- data.frame( time = runif( 100, 1, 5 ), event = sample( 0:1, 100, replace = TRUE ),
x1 = c( NA, rnorm( 99 ) ), x2 = as.factor( sample( 0:1, 100, replace = TRUE ) ),
ID = c( 1:70, 1:30 ) )

dd <- datadist( RawData )
options( datadist = "dd" )

cph( Surv( time, event ) ~ x1 + x2 + cluster( ID ), data = RawData )

It is not even an error, but a warning, but it nevertheless shouldn't be there. After some experimentation I think the problem is with the presence of a missing value and cluster().

The problem seems to occur at the very end of rms:::Design. I think the problem is that jz will index every right hand side term (including the cluster), but fname will only contain the explanatory variables (without the cluster). Thus
names(nmiss)[jz] <- fname[asm != 9]
will try to assign a length-2 vector to one of length 3.

[harrelfe]

The rms package prefers for you to handling cluster after the fit using robcov or bootcov.

[tamas-ferenci]

It works with
fit <- cph( Surv( time, event ) ~ x1 + x2, data = RawData )
fit <- robcov( fit, cluster = RawData$ID )

Thank you very much!