Compare high-dimensional Cox models by model calibration

Usage

compare_by_calibrate(
  x,
  time,
  event,
  model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad",
    "snet"),
  method = c("fitting", "bootstrap", "cv", "repeated.cv"),
  boot.times = NULL,
  nfolds = NULL,
  rep.times = NULL,
  pred.at,
  ngroup = 5,
  seed = 1001,
  trace = TRUE
)

Arguments

x: Matrix of training data used for fitting the model; on which to run the calibration.
time: Survival time. Must be of the same length with the number of rows as x.
event: Status indicator, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x.
model.type: Model types to compare. Could be at least two of "lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", or "snet".
method: Calibration method. Could be "bootstrap", "cv", or "repeated.cv".
boot.times: Number of repetitions for bootstrap.
nfolds: Number of folds for cross-validation and repeated cross-validation.
rep.times: Number of repeated times for repeated cross-validation.
pred.at: Time point at which calibration should take place.
ngroup: Number of groups to be formed for calibration.
seed: A random seed for cross-validation fold division.
trace: Logical. Output the calibration progress or not. Default is TRUE.

Examples

data(smart)
x <- as.matrix(smart[, -c(1, 2)])
time <- smart$TEVENT
event <- smart$EVENT

# Compare lasso and adaptive lasso by 5-fold cross-validation
cmp.cal.cv <- compare_by_calibrate(
  x, time, event,
  model.type = c("lasso", "alasso"),
  method = "fitting",
  pred.at = 365 * 9, ngroup = 5, seed = 1001
)
#> Starting model 1 : lasso 
#> Start fitting ...
#> Starting model 2 : alasso 
#> Start fitting ...

print(cmp.cal.cv)
#> High-Dimensional Cox Model Calibration Object
#> Random seed: 1001 
#> Calibration method: fitting
#> Model type: lasso 
#> glmnet model alpha: 1 
#> glmnet model lambda: 0.01226207 
#> glmnet model penalty factor: not specified
#> Calibration time point: 3285 
#> Number of groups formed for calibration: 5 
#> 
#> High-Dimensional Cox Model Calibration Object
#> Random seed: 1001 
#> Calibration method: fitting
#> Model type: alasso 
#> glmnet model alpha: 1 
#> glmnet model lambda: 0.03827431 
#> glmnet model penalty factor: specified
#> Calibration time point: 3285 
#> Number of groups formed for calibration: 5 
#> 
summary(cmp.cal.cv)
#>   Model type: lasso 
#>   Calibration Summary Table
#>   Predicted  Observed Lower 95% Upper 95%
#> 1 0.5721389 0.4704003 0.4008863 0.5519681
#> 2 0.7055872 0.6935569 0.5661697 0.8496059
#> 3 0.7649804 0.8020453 0.7475810 0.8604775
#> 4 0.8085048 0.8355332 0.7474947 0.9339408
#> 5 0.8518968 0.9086230 0.8763437 0.9420912
#> 
#>   Model type: alasso 
#>   Calibration Summary Table
#>   Predicted  Observed Lower 95% Upper 95%
#> 1 0.5495367 0.4698499 0.3941648 0.5600675
#> 2 0.7209324 0.7187952 0.6535857 0.7905109
#> 3 0.7933952 0.8360191 0.7855318 0.8897513
#> 4 0.8365830 0.8180967 0.7180537 0.9320781
#> 5 0.8770442 0.9234083 0.8925803 0.9553010
#> 
plot(cmp.cal.cv)