Converts longitudinal data for a msm model fit, where observations
represent the exact transition times of the process, to counting process
data.
This enables, for example, flexible parametric multi-state models to
be fitted with flexsurvreg from the
flexsurv package, or semiparametric models to be implemented with
coxph and the mstate package.
msm2Surv(data, subject, time, state, covs, Q)Data frame in the format expected by a msm
model fit with exacttimes=TRUE or all obstype=2.
Each row represents an observation of a state, and the time variable
contains the exact and complete transition times of the underlying process.
This is explained in more detail
in the help page for msm, section obstype=2.
Name of the subject ID in the data (character format, i.e. quoted).
Name of the time variable in the data (character).
Name of the state variable in the data (character).
Vector of covariate names to carry through (character). If not supplied, this is taken to be all remaining variables in the data.
Transition intensity matrix. This should have number of rows
and number of columns both equal to the number of states. If an
instantaneous transition is not allowed from state \(r\) to state
\(s\), then Q should have \((r,s)\) entry 0, otherwise it
should be non-zero. The diagonal entries are ignored.
A data frame of class "msdata", with rows representing observed or censored transitions.
There will be one row for each observed transition in the original
data, and additional rows for every potential transition that could have
occurred out of each observed state.
The data frame will have columns called:
Subject ID
Starting state of the transition
Finishing state of the transition
The starting time of the transition
The finishing time of the transition
The time difference = Tstop - Tstart
Event or censoring indicator, with 1 indicating an observed transition, and 0 indicating censoring
Transition number
and any remaining columns will represent covariates. Any covariates whose names clash with the standard variables in the returned data ("id", "from", "to", "Tstart", "Tstop", "time", "status" or "trans") have ".2" appended to their names.
The transition matrix in mstate format is stored in the trans attribute of the returned object. See the example code below.
For example, if the data supplied to msm look like this:
subj |
days |
status |
age |
treat |
| 1 | 0 | 1 | 66 | 1 |
| 1 | 27 | 2 | 66 | 1 |
| 1 | 75 | 3 | 66 | 1 |
| 1 | 97 | 4 | 66 | 1 |
| 1 | 1106 | 4 | 69 | 1 |
| 2 | 0 | 1 | 49 | 0 |
| 2 | 90 | 2 | 49 | 0 |
| 2 | 1037 | 2 | 51 | 0 |
then the output of msm2Surv will be a data frame looking like this:
id |
from |
to |
Tstart |
Tstop |
time |
status |
age |
treat |
trans |
1 |
| 1 | 2 | 0 | 27 | 27 | 1 | 66 | 1 | 1 | 1 | 1 |
| 4 | 0 | 27 | 27 | 0 | 66 | 1 | 2 | 1 | 2 | 3 |
| 27 | 75 | 48 | 1 | 66 | 1 | 3 | 1 | 2 | 4 | 27 |
| 75 | 48 | 0 | 66 | 1 | 4 | 1 | 3 | 4 | 75 | 97 |
| 22 | 1 | 69 | 1 | 5 | 2 | 1 | 2 | 0 | 90 | 90 |
| 1 | 49 | 0 | 1 | 2 | 1 | 4 | 0 | 90 | 90 | 0 |
| 49 | 0 | 2 | 2 | 2 | 3 | 90 | 1037 | 947 | 0 | 49 |
| 0 | 3 | 2 | 2 | 4 | 90 | 1037 | 947 | 0 | 49 | 0 |
| 4 |
id |
from |
to |
Tstart |
Tstop |
time |
status |
age |
treat |
trans |
At 27 days, subject 1 is observed to move from state 1 to state 2 (first row, status 1), which means that their potential transition from state 1 to state 4 is censored (second row, status 0).
See the mstate package and the references below for more details of this data format and using it for semi-parametric multi-state modelling.
Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26: 2389-2430.
Liesbeth C. de Wreede, Marta Fiocco, Hein Putter (2011). mstate: An R Package for the Analysis of Competing Risks and Multi-State Models. Journal of Statistical Software, 38(7), 1-30. http://www.jstatsoft.org/v38/i07
Jackson, C. H. (2014). flexsurv: Flexible parametric survival and multi-state models. R package version 0.5.
msprep, in mstate, which produces data in a similar format,
given data in "wide" format with one row per subject.
# NOT RUN {
msmdat <- data.frame(
subj = c(1, 1, 1, 1, 1, 2, 2, 2),
days = c(0, 27, 75, 97, 1106, 0, 90, 1037),
status = c(1, 2, 3, 4, 4, 1, 2, 2),
age = c(66, 66, 66, 66, 69, 49, 49, 51),
treat = c(1, 1, 1, 1, 1, 0, 0, 0)
)
# transitions only allowed to next state up or state 4
Q <- rbind(c(1, 1, 0, 1),
c(0, 1, 1, 1),
c(0, 0, 1, 1),
c(0, 0, 0, 0))
dat <- msm2Surv(data=msmdat, subject="subj", time="days", state="status",
Q=Q)
dat
attr(dat, "trans")
# }
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