msm2Surv | R Documentation |
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 = NULL, Q)
data |
Data frame in the format expected by a |
subject |
Name of the subject ID in the data (character format, i.e. quoted). |
time |
Name of the time variable in the data (character). |
state |
Name of the state variable in the data (character). |
covs |
Vector of covariate names to carry through (character). If not supplied, this is taken to be all remaining variables in the data. |
Q |
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 |
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 |
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.
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:
id |
Subject ID |
from |
Starting state of the transition |
to |
Finishing state of the transition |
Tstart |
The starting time of the transition |
Tstop |
The finishing time of the transition |
time |
The time difference = |
status |
Event or censoring indicator, with 1 indicating an observed transition, and 0 indicating censoring |
trans |
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.
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk
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.
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.
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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