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,
  rule = c("lambda.min", "lambda.1se"),
  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.
rule: Model selection criterion for glmnet models, `"lambda.min"` or `"lambda.1se"`. Defaults to `"lambda.min"`.
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: 0.002767573 
#> 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: 0.001769366 
#> 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 0.5420008 0.4469568 0.3681894 0.5425751
#> 2 0.7165334 0.7567249 0.6955663 0.8232608
#> 3 0.7901613 0.7870803 0.7124101 0.8695768
#> 4 0.8408859 0.8919718 0.8514594 0.9344117
#> 5 0.8894957 0.9228286 0.8912396 0.9555373
#> 
#>   Model type: alasso 
#>   Calibration Summary Table
#>   Predicted  Observed Lower 95% Upper 95%
#> 1 0.5343091 0.4483935 0.3676640 0.5468490
#> 2 0.7242445 0.7489438 0.6896464 0.8133396
#> 3 0.8011894 0.8030839 0.7253344 0.8891673
#> 4 0.8515505 0.8872493 0.8446693 0.9319758
#> 5 0.8996483 0.9225448 0.8903529 0.9559006
#> 
plot(cmp.cal.cv)