Simulation of Output from Cumulative Incidence Regression Model

Simulates data that looks like fit from a fitted cumulative incidence model (Fine-Gray or logistic).

Usage

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

Arguments

cif

Output from prop.odds.subdist or ccr (cmprsk), or call invsubdist with cumulative and linear predictor.

n

Number of simulations.

data

Data frame to extract covariates.

Z

Design matrix instead of data.

rr

Relative risks.

strata

Strata vector.

drawZ

Logical; randomly sample from Z.

cens

Censoring specification.

rrc

Relative risks for censoring.

entry

Delayed entry time.

Sentry

Survival related to delayed entry.

cumstart

Start cumulatives at time 0.

U

Uniforms for drawing timing.

pU

Uniforms for drawing event type.

type

Model type: "logistic", "cloglog", or "rr".

extend

Extend piecewise constant with constant rate.

...

Arguments for sim_subdist.

Value

Data frame with simulated event times, status, and covariates.

See also

[simul_cifs()]

Author

Thomas Scheike

Examples

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 -0.9872 -0.07274 0.0231012
#> age        0.3553 0.09611  0.1669  0.54363 0.0002187
plot(cif,col=1)
simbmt <- sim_cif(cif,nsim,data=bmt)
dtable(simbmt,~cause)
#> 
#> cause
#>  0  1 
#> 61 39 
#> 
scif <- cifreg(Event(time,cause)~platelet+age,data=simbmt,cause=1)
estimate(scif)
#>          Estimate Std.Err     2.5%  97.5% P-value
#> platelet  -0.3317  0.4475 -1.20884 0.5454 0.45855
#> age        0.4225  0.2433 -0.05446 0.8994 0.08253
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)  
#> Warning: IC does not have mean zero (max |mean|/rms = 6.1e-05). Using lava.options(check.ic = FALSE) disables the warning globally.
#>     Estimate Std.Err   2.5%  97.5%   P-value
#> age   0.3584 0.07883 0.2039 0.5129 5.471e-06
plot(cif,col=1)
simbmt <- sim_cif(cif,nsim,data=bmt)
scif <- cifregFG(Event(time,cause)~strata(tcell)+age,data=simbmt,cause=1)
estimate(scif)
#>     Estimate Std.Err    2.5%  97.5% P-value
#> age   0.1246  0.1446 -0.1588 0.4081  0.3887
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)
scif1 <-  cifreg(Event(time,cause)~strata(tcell)+age,data=simbmt,cause=1)
scif2 <-  cifreg(Event(time,cause)~strata(platelet)+tcell+age,data=simbmt,cause=2)
cbind(cif1$coef,scif1$coef)   
#>          [,1]      [,2]
#> age 0.4157207 0.5536631
## can be off due to restriction F1+F2<= 1    
cbind(cif2$coef,scif2$coef)   
#>              [,1]       [,2]
#> tcell  0.68378716  1.2132311
#> age   -0.03484858 -0.4479749
    
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)