R: Time based merge for survival data

tmerge {survival} | R Documentation

## 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
`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.

### 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) 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 the `tdcstart` option and is reset 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) Similar to tdc, except that the event count is accumulated over time for each subject.

  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 `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.

### Value

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. 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", for 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.

### Author(s)

Terry Therneau

### See Also

`neardate`

### Examples

    # 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)

[Package survival version 3.2-7 Index]