Time based merge for survival data
A common task in survival analysis is the creation of start,stop data sets which have multiple intervals for each subject, along with the covariate values that apply over that interval. This function aids in the creation of such data sets.
tmerge(data1, data2, id,..., tstart, tstop, options)
data1 |
the primary data set, to which new variables and/or observation will be added |
data2 |
second data set in which all the other arguments will be found |
id |
subject identifier |
... |
operations that add new variables or intervals, see below |
tstart |
optional variable to define the valid time range for each subject, only used on an initial call |
tstop |
optional variable to define the valid time range for each subject, only used on an initial call |
options |
a list of options. Valid ones are idname, tstartname, tstopname, delay, na.rm, and tdcstart. See the explanation below. |
The program is often run in multiple passes, the first of which defines the basic structure, and subsequent ones that add new variables to that structure. For a more complete explanation of how this routine works refer to the vignette on time-dependent variables.
There are 4 types of operational arguments: a time dependent covariate (tdc), cumulative count (cumtdc), event (event) or cumulative event (cumevent). Time dependent covariates change their values before an event, events are outcomes.
newname = tdc(y, x, init) A new time dependent covariate
variable will created.
The argument y is assumed to be on the
scale of the start and end time, and each instance describes the
occurrence of a "condition" at that time.
The second argument x is optional. In the case where
x is missing the count variable starts at 0 for each subject
and becomes 1 at the time of the event.
If x is present the value of the time dependent covariate
is initialized to value of init, if present, or
the tdcstart option otherwise, and is updated to the
value of x at each observation.
If the option na.rm=TRUE missing values of x are
first removed, i.e., the update will not create missing values.
newname = cumtdc(y,x, init) Similar to tdc, except that the event
count is accumulated over time for each subject. The variable
x must be numeric.
newname = event(y,x) Mark an event at time y.
In the usual case that x is missing the new 0/1 variable
will be similar to the 0/1 status variable of a survival time.
newname = cumevent(y,x) Cumulative events.
The function adds three new variables to the output data set:
tstart, tstop, and id. The options argument
can be used to change these names.
If, in the first call, the id argument is a simple name, that
variable name will be used as the default for the idname option.
If data1 contains the tstart variable then that is used as
the starting point for the created time intervals, otherwise the initial
interval for each id will begin at 0 by default.
This will lead to an invalid interval and subsequent error if say a
death time were <= 0.
The na.rm option affects creation of time-dependent covariates.
Should a data row in data2 that has a missing value for the
variable be ignored (na.rm=FALSE, default) or should it generate an
observation with a value of NA? The default value leads to
"last value carried forward" behavior.
The delay option causes a time-dependent covariate's new
value to be delayed, see the vignette for an example.
a data frame with two extra attributes tname and
tcount.
The first contains the names of the key variables; it's persistence
from call to call allows the user to avoid constantly reentering the
options argument.
The tcount variable contains counts of the match types.
New time values that occur before the first interval for a subject
are "early", those after the last interval for a subject are "late",
and those that fall into a gap are of type "gap".
All these are are considered to be outside the specified time frame for the
given subject. An event of this type will be discarded.
An observation in data2 whose identifier matches no rows in
data1 is of type "missid" and is also discarded.
A time-dependent covariate value will be applied to later intervals but
will not generate a new time point in the output.
The most common type will usually be "within", corresponding to
those new times that
fall inside an existing interval and cause it to be split into two.
Observations that fall exactly on the edge of an interval but within the
(min, max] time for a subject are counted
as being on a "leading" edge, "trailing" edge or "boundary".
The first corresponds for instance
to an occurrence at 17 for someone with an intervals of (0,15] and (17, 35].
A tdc at time 17 will affect this interval
but an event at 17 would be ignored. An event
occurrence at 15 would count in the (0,15] interval.
The last case is where the main data set has touching
intervals for a subject, e.g. (17, 28] and (28,35] and a new occurrence
lands at the join. Events will go to the earlier interval and counts
to the latter one. A last column shows the number of additions
where the id and time point were identical.
When this occurs, the tdc and event operators will use
the final value in the data (last edit wins), but ignoring missing,
while cumtdc and cumevent operators add up the values.
These extra attributes are ephemeral and will be discarded if the dataframe is modified. This is intentional, since they will become invalid if for instance a subset were selected.
Terry Therneau
# The pbc data set contains baseline data and follow-up status
# for a set of subjects with primary biliary cirrhosis, while the
# pbcseq data set contains repeated laboratory values for those
# subjects.
# The first data set contains data on 312 subjects in a clinical trial plus
# 106 that agreed to be followed off protocol, the second data set has data
# only on the trial subjects.
temp <- subset(pbc, id <= 312, select=c(id:sex, stage)) # baseline data
pbc2 <- tmerge(temp, temp, id=id, endpt = event(time, status))
pbc2 <- tmerge(pbc2, pbcseq, id=id, ascites = tdc(day, ascites),
bili = tdc(day, bili), albumin = tdc(day, albumin),
protime = tdc(day, protime), alk.phos = tdc(day, alk.phos))
fit <- coxph(Surv(tstart, tstop, endpt==2) ~ protime + log(bili), data=pbc2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.