rms (version 6.0-1)

ie.setup: Intervening Event Setup

Description

Creates several new variables which help set up a dataset for modeling with cph or coxph when there is a single binary time-dependent covariable which turns on at a given time, and stays on. This is typical when analyzing the impact of an intervening event. ie.setup creates a Surv object using the start time, stop time format. It also creates a binary indicator for the intervening event, and a variable called subs that is useful when attach-ing a dataframe. subs has observation numbers duplicated for subjects having an intervening event, so those subject's baseline covariables (that are not time-dependent) can be duplicated correctly.

Usage

ie.setup(failure.time, event, ie.time, break.ties=FALSE)

Arguments

failure.time

a numeric variable containing the event or censoring times for the terminating event

event

a binary (0/1) variable specifying whether observations had the terminating event (event=1) or were censored (event=0)

ie.time

intervening event times. For subjects having no intervening events, the corresponding values of ie.time must be NA.

break.ties

Occasionally intervening events are recorded as happening at exactly the same time as the termination of follow-up for some subjects. The Surv and Surv functions will not allow this. To randomly break the ties by subtracting a random number from such tied intervening event times, specify break.ties=TRUE. The random number is uniform between zero and the minimum difference between any two untied failure.times.

Value

a list with components S, ie.status, subs, reps. S is a Surv object containing start and stop times for intervals of observation, along with event indicators. ie.status is one if the intervening event has occurred at the start of the interval, zero otherwise. subs is a vector of subscripts that can be used to replicate other variables the same way S was replicated. reps specifies how many times each original observation was replicated. S, ie.status, subs are all the same length (at least the number of rows for S is) and are longer than the original failure.time vector. reps is the same length as the original failure.time vector. The subs vector is suitable for passing to validate.lrm or calibrate, which pass this vector under the name cluster on to predab.resample so that bootstrapping can be done by sampling with replacement from the original subjects rather than from the individual records created by ie.setup.

See Also

cph, coxph, Surv, cr.setup, predab.resample

Examples

# NOT RUN {
failure.time <- c(1 ,   2,   3)
event        <- c(1 ,   1,   0)
ie.time      <- c(NA, 1.5, 2.5)

z <- ie.setup(failure.time, event, ie.time)
S <- z$S
S
ie.status <- z$ie.status
ie.status
z$subs
z$reps
# }
# NOT RUN {
attach(input.data.frame[z$subs,])   #replicates all variables
f <- cph(S ~ age + sex + ie.status)
# Instead of duplicating rows of data frame, could do this:
attach(input.data.frame)
z <- ie.setup(failure.time, event, ie.time)
s <- z$subs
age <- age[s]
sex <- sex[s]
f <- cph(S ~ age + sex + ie.status)
# }