pencal (version 2.3.0)

performance_pencox: Predictive performance of the penalized Cox model with time-independent covariates

Description

This function computes the naive and optimism-corrected measures of performance (C index, time-dependent AUC and time-dependent Brier score) for a penalized Cox model with time-independent covariates. The optimism correction is computed based on a cluster bootstrap optimism correction procedure (CBOCP, Signorelli et al., 2021)

Usage

performance_pencox(fitted_pencox, metric = c("tdauc", "c", "brier"),
  times = c(2, 3), n.cores = 1, verbose = TRUE)

Value

A list containing the following objects:

• call: the function call;

• concordance: a data frame with the naive and optimism-corrected estimates of the concordance (C) index;

• tdAUC: a data frame with the naive and optimism-corrected estimates of the time-dependent AUC at the desired time points.

Arguments

fitted_pencox

the output of pencox

metric

the desired performance measure(s). Options include: 'tdauc', 'c' and 'brier'

times

numeric vector with the time points at which to estimate the time-dependent AUC and time-dependent Brier score

n.cores

number of cores to use to parallelize part of the computations. If ncores = 1 (default), no parallelization is done. Pro tip: you can use parallel::detectCores() to check how many cores are available on your computer

verbose

if TRUE (default and recommended value), information on the ongoing computations is printed in the console

Author

Mirko Signorelli

References

Signorelli, M. (2024). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. The R Journal, 16 (2), 134-153.

Signorelli, M., Spitali, P., Al-Khalili Szigyarto, C, The MARK-MD Consortium, Tsonaka, R. (2021). Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data. Statistics in Medicine, 40 (27), 6178-6196.

See Also

pencox

Examples

# generate example data
set.seed(1234)
p = 4 # number of longitudinal predictors
simdata = simulate_prclmm_data(n = 100, p = p, p.relev = 2, 
             seed = 123, t.values = c(0, 0.5, 1, 1.5, 2))
# create dataframe with baseline measurements only
baseline.visits = simdata$long.data[which(!duplicated(simdata$long.data$id)),]
df = merge(simdata$surv.data, baseline.visits, by = 'id')
df = df[ , -c(5:6)]

do.bootstrap = FALSE
# IMPORTANT: set do.bootstrap = TRUE to compute the optimism correction!
n.boots = ifelse(do.bootstrap, 100, 0)
more.cores = FALSE
# IMPORTANT: set more.cores = TRUE to speed computations up!
if (!more.cores) n.cores = 2
if (more.cores) {
   # identify number of available cores on your machine
   n.cores = parallel::detectCores()
   if (is.na(n.cores)) n.cores = 2
}

form = as.formula(~ baseline.age + marker1 + marker2
                     + marker3 + marker4)
base.pcox = pencox(data = df, 
              formula = form, 
              n.boots = n.boots, n.cores = n.cores) 
ls(base.pcox)
                   
# compute the performance measures
perf = performance_pencox(fitted_pencox = base.pcox, 
          metric = 'tdauc', times = 3:5, n.cores = n.cores)
 # use metric = 'brier' for the Brier score and metric = 'c' for the
 # concordance index

# time-dependent AUC estimates:
ls(perf)
perf$tdAUC