msm2Surv: Convert data for `msm' to data for `survival' and `mstate' analysis

Description

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, semiparametric models to be fitted with coxph, and subsequent multi-state prediction with the mstate package.

Usage

msm2Surv(data, subject, time, state, covs, Q)

Arguments

data

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

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.

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

Value

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:
idSubject ID
fromStarting state of the transition
toFinishing state of the transition
TstartThe starting time of the transition
TstopThe finishing time of the transition
timeThe time difference = Tstop - Tstart
statusEvent or censoring indicator, with 1 indicating an observed transition, and 0 indicating censoring
transTransition 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.

Details

For example, if the data supplied to msm look like this:

lllll{ 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:

lllllllllll{ 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.

References

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

Examples

Run this code

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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