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Simulation of output from Cumulative incidence regression model

Source: R/sim-pc-hazard.R
sim.cif.Rd

Simulates data that looks like fit from fitted cumulative incidence model

Usage

sim.cif(
  cif,
  n,
  data = NULL,
  Z = NULL,
  rr = NULL,
  strata = NULL,
  drawZ = TRUE,
  cens = NULL,
  rrc = NULL,
  cumstart = c(0, 0),
  U = NULL,
  pU = NULL,
  type = NULL,
  extend = NULL,
  ...
)

Arguments

cif

output form prop.odds.subdist or ccr (cmprsk), can also call invsubdist with with cumulative and linear predictor

n

number of simulations.

data

to extract covariates for simulations (draws from observed covariates).

Z

to use these covariates for simulation rather than drawing new ones.

rr

possible vector of relative risk for cox model.

strata

possible vector of strata

drawZ

to random sample from Z or not

cens

specifies censoring model, if "is.matrix" then uses cumulative hazard given, if "is.scalar" then uses rate for exponential, and if not given then takes average rate of in simulated data from cox model.

rrc

possible vector of relative risk for cox-type censoring.

cumstart

to start cumulatives at time 0 in 0.

U

uniforms to use for drawing of timing for cumulative incidence.

pU

uniforms to use for drawing event type (F1,F2,1-F1-F2).

type

of model logistic,cloglog,rr

extend

to extend piecewise constant with constant rate. Default is average rate over time from cumulative (when TRUE), if numeric then uses given rate.

...

arguments for simsubdist (for example Uniform variable for realizations)

Author

Thomas Scheike

Examples

library(mets)
data(bmt)
nsim <- 100

## logit cumulative incidence regression model 
cif <- cifreg(Event(time,cause)~platelet+age,data=bmt,cause=1)
estimate(cif)  
#>          Estimate Std.Err    2.5%    97.5%   P-value
#> platelet  -0.5300 0.23329 -0.9873 -0.07275 0.0230973
#> age        0.3553 0.09611  0.1669  0.54365 0.0002187
plot(cif,col=1)
simbmt <- sim.cif(cif,nsim,data=bmt)
dtable(simbmt,~status)
#> 
#> status
#>  0  1 
#> 62 38 
#> 
scif <- cifreg(Event(time,status)~platelet+age,data=simbmt,cause=1)
estimate(scif)
#>          Estimate Std.Err    2.5%  97.5% P-value
#> platelet   0.1401  0.4363 -0.7150 0.9953 0.74808
#> age        0.4783  0.2072  0.0722 0.8843 0.02097
plot(scif,add=TRUE,col=2)


## Fine-Gray cloglog cumulative incidence regression model 
cif <- cifregFG(Event(time,cause)~strata(tcell)+age,data=bmt,cause=1)
estimate(cif)  
#>     Estimate Std.Err   2.5%  97.5%   P-value
#> age   0.3584 0.07883 0.2039 0.5129 5.469e-06
plot(cif,col=1)
simbmt <- sim.cif(cif,nsim,data=bmt)
scif <- cifregFG(Event(time,status)~strata(tcell)+age,data=simbmt,cause=1)
estimate(scif)
#>     Estimate Std.Err     2.5%  97.5% P-value
#> age   0.3866  0.1936 0.007124 0.7661 0.04585
plot(scif,add=TRUE,col=2)


################################################################
#  simulating several causes with specific cumulatives 
################################################################
cif1 <-  cifreg(Event(time,cause)~strata(tcell)+age,data=bmt,cause=1)
cif2 <-  cifreg(Event(time,cause)~strata(platelet)+tcell+age,data=bmt,cause=2)
cifss <-  list(cif1,cif2)
simbmt <- sim.cifs(list(cif1,cif2),nsim,data=bmt,extend=0.005)
dtable(simbmt,~status)
#> 
#> status
#>  0  1  2 
#> 49 41 10 
#> 
scif1 <-  cifreg(Event(time,status)~strata(tcell)+age,data=simbmt,cause=1)
scif2 <-  cifreg(Event(time,status)~strata(platelet)+tcell+age,data=simbmt,cause=2)
cbind(cif1$coef,scif1$coef)   
#>          [,1]      [,2]
#> age 0.4157306 0.6036799
## can be off due to restriction F1+F2<= 1    
cbind(cif2$coef,scif2$coef)   
#>              [,1]      [,2]
#> tcell  0.68379711 1.2507661
#> age   -0.03484497 0.9837646
    
par(mfrow=c(1,2))   
## Cause 1 follows the model    
plot(cif1); plot(scif1,add=TRUE,col=1:2,lwd=2)
# Cause 2:second cause is modified with restriction to satisfy F1+F2<= 1, so scaled down     
plot(cif2); plot(scif2,add=TRUE,col=1:2,lwd=2)