Split a survival data set at specified times

Given a survival data set and a set of specified cut times, split each record into multiple subrecords at each cut time.

Usage

survSplit(formula, data, subset, na.action=na.pass,
            id, cut, zero=0, episode, start="tstart",
          end="tstop", event="event", added, timefix=TRUE)

Arguments

formula: a model formula
data: a data frame
subset, na.action: rows of the data to be retained
id: name of an id variable, normally found in the data frame. Necessary for timeline data.
cut: the vector of timepoints to cut at
zero: For simple Surv(time, status) data, the starting point for the first interval.
episode: character string with the name of new episode variable (optional)
start: character string with the name of a start time variable (will be created if needed)
end: character string with the name of event time variable
event: character string with the name of censoring indicator
added: character string with the name of the new "this observation was added" variable (optional)
timefix: process times through the aeqSurv function to eliminate potential roundoff issues.

Value

New, longer data frame.

Details

Each interval in the original data is cut at the given points; if an original row were (15, 60] with a cut vector of (10,30, 40) the resulting data set would have intervals of (15,30], (30,40] and (40, 60].

Each row in the final data set will lie completely within one of the cut intervals. Which interval for each row of the output is shown by the episode variable, where 1= less than the first cutpoint, 2= between the first and the second, etc. For the example above the values would be 2, 3, and 4. Whether a given row was added is shown by the added variable.

The routine is called with a formula as the first argument. The right hand side of the formula can be used to delimit variables that should be retained; normally one will use ~ . as a shorthand to retain them all.

The most common call would have Surv(name1, name2, name3) or Surv2(name1, name2) as the left hand side, where name1, name2, name3 are simple variable names; in that case the output will have the same variable names. For a Surv(name1, name2) call a new variable with the name given by start option will be added to the data. However, the routine is not sophisticated; it only does this substitution for simple names. A call of Surv(time, stat==2) for instance will not retain "stat" as the name of the event variable, it will instead use the name found in the event argument. If the response variable is the variable names of a Surv or Surv2 object then the updated response will have the same name.

Rows of data with a missing time or status are copied across unchanged, unless the na.action argument is changed from its default value of na.pass. But in the latter case any row that is missing for any variable will be removed, which is rarely what is desired.

An older, now deprecated usage is a call without a formula, e.g., survSplit(veteran, time="time", event="status", cut=c(90,180))

Examples

fit1 <- coxph(Surv(time, status) ~ karno + age + trt, veteran)
plot(cox.zph(fit1)[1])

# a cox.zph plot of the data suggests that the effect of Karnofsky score
#  begins to diminish by 60 days and has faded away by 120 days.
# Fit a model with separate coefficients for the three intervals.
#
vet2 <- survSplit(Surv(time, status) ~., veteran,
                   cut=c(60, 120), episode ="timegroup")
fit2 <- coxph(Surv(tstart, time, status) ~ karno* strata(timegroup) +
                age + trt, data= vet2)
c(overall= coef(fit1)[1],
  t0_60  = coef(fit2)[1],
  t60_120= sum(coef(fit2)[c(1,4)]),
  t120   = sum(coef(fit2)[c(1,5)]))
#> overall.karno   t0_60.karno       t60_120          t120 
#>  -0.034443897  -0.049176157  -0.011031558  -0.007629841 

# Sometimes we want to split on one scale and analyse on another
#  Add a "current age" variable to the mgus2 data set.
temp1 <- mgus2
temp1$endage <- mgus2$age + mgus2$futime/12    # futime is in months
temp1$startage <- temp1$age
temp2 <- survSplit(Surv(age, endage, death) ~ ., temp1, cut=25:100,
                   start= "age1", end= "age2")

# restore the time since enrollment scale
temp2$time1 <- (temp2$age1 - temp2$startage)*12
temp2$time2 <- (temp2$age2 - temp2$startage)*12

# In this data set, initial age and current age have similar utility
mfit1 <- coxph(Surv(futime, death) ~ age + sex, data=mgus2)
mfit2 <- coxph(Surv(time1, time2, death) ~ age1 + sex, data=temp2)