fcut {Epi} | R Documentation |
This function cuts the follow-up time at multiple failure times
allowing a person to stay at risk between and after the laset
failure. It is aimed at processing of recurrent events. Failure times
outside the interval (enter
,exit
) are ignored.
fcut( enter, exit, dof, fail = 0, data = data.frame(enter, exit), Expand = 1:nrow( data ))
enter |
Date of entry into the study. Numerical vector. |
exit |
Date of exit from the study. Numerical vector. |
fail |
Failure indicator for the exit date. |
dof |
Failure time(s). For multiple failures per individual,
dof must be a list. |
data |
Dataframe of variables to be carried over to the output. |
Expand |
Variable identifying original records. |
A dataframe with the same variables as in data
preceded by the
variables:
Expand |
Identification of the rows from the input dataframe. |
Enter |
Entry date for the interval. |
Exit |
Exit date for the interval. |
Fail |
Failure indicator for end of the current interval. |
n.Fail |
Number of failures prior to the start of the current
interval. Counts all failures given in the list dof ,
including those prior to enter . |
Bendix Carstensen, Steno Diabetes Center, bxc@steno.dk, www.biostat.ku.dk/~bxc
one <- round( runif( 15, 0, 10 ), 1 ) two <- round( runif( 15, 0, 10 ), 1 ) doe <- pmin( one, two ) dox <- pmax( one, two ) # Goofy data rows to test possibly odd behaviour doe[1:3] <- dox[1:3] <- 8 dox[2] <- 6 dox[3] <- 7.5 # Some failure indicators fail <- sample( 0:1, 15, replace=TRUE, prob=c(0.7,0.3) ) # Failure times in a list dof <- sample( c(one,two), 15 ) l.dof <- list( f1=sample( c(one,two), 15 ), f2=sample( c(one,two), 15 ), f3=sample( c(one,two),15 ) ) # The same, but with events prior to entry removed lx.dof <- lapply( l.dof, FUN=function(x){ x[x<doe] <- NA ; x } ) # So what have we got data.frame( doe, dox, fail, l.dof, lx.dof ) # Cut follow-up at event times fcut( doe, dox, lx.dof, fail, data=data.frame( doe, dox, lx.dof ) )