tmerge: Time based merge for survival data

Description

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.

Usage

tmerge(data1, data2,  id,…, tstart, tstop, options)

Arguments

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

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.

Value

a data frame with two extra attributes tm.retain and tcount. The first contains the names of the key variables, and which names correspond to tdc or event variables. 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.

Details

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 or should it generate an observation with a value of NA? The default of TRUE causes the last non-missing value to be carried forward. The delay option causes a time-dependent covariate's new value to be delayed, see the vignette for an example.

Examples

Run this code

# NOT RUN {
# 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)
# }