icmstate (version 0.2.0)

probtrans_coxph: Calculate subject specific transition probabilities from a multi-state coxph model.

Description

Given a coxph model fit on multi-state data (prepared with msprep), determine transition probabilities for subjects in newdata.

Usage

probtrans_coxph(
  object,
  predt,
  direction = c("forward", "fixedhorizon"),
  newdata,
  trans
)

Value

An object of class "probtrans.subjects". This is a list of length n (number of subjects in newdata), with each list element an object of class probtrans for the associated subject. List elements can be accessed using [[x]], with x

ranging from 1 to n. Additionally, each list element has an element $id, representing the subject id and the output object also has an element $subject_ids representing the subject ids in order.

Arguments

object

A coxph object fit on multi-state data. Must contain a strata(X) term. Data used for the coxph() fit preferably prepared using msprep.

predt

A positive number indicating the prediction time. This is either the time at which the prediction is made (if direction = "forward") or the time for which the prediction is to be made (if direction = "backward").

direction

One of "forward" (default) or "fixedhorizon", indicating whether prediction is forward or for a fixed horizon

newdata

A data.frame containing a single row for each transition per subject in the data. For a model with m possible transitions, and n subjects newdata must have m*n rows. It must contain the following named columns:

id:

Unique identifier of the subject, can be numeric or character;

from:

State from which the transition takes place;

to:

State to which the transition takes place;

trans:

Transition number in the 'transMat' trans this transition relates to;

"variables":

The variables and their values for the subject identified by "id" for the transition this entry relates to. Names must match the names of the variables in coxph object.

Note that newdata must contain a column containing the variable which was used to determine the stratum of a transition in object. Usually the stratum is determined from one of the required columns. The "variables" columns can usually be obtained using expand.covs.

trans

A transition matrix as created by transMat.

Details

When using this function for newdata with many subjects, consider running the function multiple times for parts of newdata to negate the risk of running our of memory.

Examples

#Example from the mstate vignette
#We determine the subject specific transition probabilities for subjects
#in the ebmt3 data-set 
if(require("mstate")){
  data(ebmt3)
  n <- nrow(ebmt3)
  tmat <- transMat(x = list(c(2, 3), c(3), c()), names = c("Tx",
                                                           "PR", "RelDeath"))
  ebmt3$prtime <- ebmt3$prtime/365.25
  ebmt3$rfstime <- ebmt3$rfstime/365.25
  covs <- c("dissub", "age", "drmatch", "tcd", "prtime")
  msbmt <- msprep(time = c(NA, "prtime", "rfstime"), status = c(NA,
                  "prstat", "rfsstat"), data = ebmt3, trans = tmat, keep = covs)
  #Expand covariates so that we can have transition specific covariates
  msbmt <- expand.covs(msbmt, covs, append = TRUE, longnames = FALSE)
  
  #Create extra variable to allow gender mismatch to have the same effect 
  #for transitions 2 and 3.
  msbmt$drmatch.2.3 <- msbmt$drmatch.2 + msbmt$drmatch.3
  
  #Introduce pr covariate for proportionality assumption of transitions 2 and 3
  #(only used in models 2 and 4)
  msbmt$pr <- 0
  msbmt$pr[msbmt$trans == 3] <- 1
  
  
  
  #-------------Models---------------------#
  
  #Simple model, transition specific covariates, each transition own baseline hazard
  c1 <- coxph(Surv(Tstart, Tstop, status) ~ dissub1.1 + dissub2.1 +
                age1.1 + age2.1 + drmatch.1 + tcd.1 + dissub1.2 + dissub2.2 +
                age1.2 + age2.2 + drmatch.2 + tcd.2 + dissub1.3 + dissub2.3 +
                age1.3 + age2.3 + drmatch.3 + tcd.3 + strata(trans), data = msbmt,
                method = "breslow")
  
  #Model with same baseline hazards for transitions 2 (1->3) and 3(2->3)
  #pr then gives the ratio of the 2 hazards for these transitions
  c2 <- coxph(Surv(Tstart, Tstop, status) ~ dissub1.1 + dissub2.1 +
                age1.1 + age2.1 + drmatch.1 + tcd.1 + dissub1.2 + dissub2.2 +
                age1.2 + age2.2 + drmatch.2 + tcd.2 + dissub1.3 + dissub2.3 +
                age1.3 + age2.3 + drmatch.3 + tcd.3 + pr + strata(to), data = msbmt,
                method = "breslow")
  
  #Same as c2, but now Gender mismatch has the same effect for both 
  c3 <- coxph(Surv(Tstart, Tstop, status) ~ dissub1.1 + dissub2.1 +
                age1.1 + age2.1 + drmatch.1 + tcd.1 + dissub1.2 + dissub2.2 +
                age1.2 + age2.2 + drmatch.2.3 + tcd.2 + dissub1.3 + dissub2.3 +
                age1.3 + age2.3  + tcd.3 + pr + strata(to), data = msbmt,
              method = "breslow")
              
  
  #Predict for first 30 people in ebmt data
  tmat2 <- to.trans2(tmat)[, c(2,3,1)]
  names(tmat2)[3] <- "trans"
  n_transitions <- nrow(tmat2)
  
  newdata <- ebmt3[rep(seq_len(30), each = nrow(tmat2)),]
  newdata <- cbind(tmat2[rep(seq_len(nrow(tmat2)), times = 30), ], newdata)
  #Make of class "msdata"
  attr(newdata, "trans") <- tmat
  class(newdata) <- c("msdata", "data.frame")
  #Covariate names to expand
  covs <- names(newdata)[5:ncol(newdata)]
  newdata <- expand.covs(newdata, covs, append = TRUE, longnames = FALSE)
  
  newdata$drmatch.2.3 <- newdata$drmatch.2 + newdata$drmatch.3
  
  newdata$pr <- 0
  newdata$pr[newdata$trans == 3] <- 1
  
  #Calculate transition probabilities for the Cox fits
  icmstate_pt1 <- probtrans_coxph(c1, predt = 0, direction = "forward", 
                                  newdata = newdata, trans = tmat)
  icmstate_pt2 <- probtrans_coxph(c2, predt = 0, direction = "forward", 
                                  newdata = newdata, trans = tmat)
  icmstate_pt3 <- probtrans_coxph(c3, predt = 0, direction = "forward", 
                                  newdata = newdata, trans = tmat)
                                  
  #Now we can plot the transition probabilities for each subject separately:
  plot(icmstate_pt1[[1]])
  #icmstate_pt has length number of subjects in newdata
  #And icmstate_pt1[[i]] is an object of class "probtrans", so you can 
  #use all probtrans functions: summary, plot etc.
  
  #Alternatively, use the plotting function directly:
  plot(icmstate_pt2, id = 2)
}