performance_pencox_baseline: Predictive performance of the penalized Cox model with baseline covariates

Description

This function computes the naive and optimism-corrected measures of performance (C index and time-dependent AUC) for a penalized Cox model with baseline covariates as presented in Signorelli et al. (2021). The optimism correction is a bootstrap optimism correction procedure

Usage

performance_pencox_baseline(fitted_pencox, times = 1, n.cores = 1,
  verbose = TRUE)

Arguments

fitted_pencox

the output of pencox_baseline

times

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

n.cores

number of cores to use to parallelize the computation of the CBOCP procedure. 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

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.

References

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. DOI: 10.1002/sim.9178

Examples

Run this code

# NOT RUN {
# 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, 0.2, 0.5, 1, 1.5, 2))
# create dataframe with baseline measurements only
baseline.visits = simdata$long.data[which(!duplicated(simdata$long.data$id)),]
df = cbind(simdata$surv.data, baseline.visits)
df = df[ , -c(5:7)]

do.bootstrap = FALSE
# IMPORTANT: set do.bootstrap = TRUE to compute the optimism correction!
n.boots = ifelse(do.bootstrap, 100, 0)
parallelize = FALSE
# IMPORTANT: set parallelize = TRUE to speed computations up!
if (!parallelize) n.cores = 1
if (parallelize) {
  # identify number of available cores on your machine
  n.cores = parallel::detectCores()
  if (is.na(n.cores)) n.cores = 1
}

form = as.formula(~ baseline.age + marker1 + marker2
                     + marker3 + marker4)
base.pcox = pencox_baseline(data = df, 
              formula = form, 
              n.boots = n.boots, n.cores = n.cores) 
ls(base.pcox)
                   
# compute the performance measures
perf = performance_pencox_baseline(fitted_pencox = base.pcox, 
          times = c(0.5, 1, 1.5, 2), n.cores = n.cores)

# concordance index:
perf$concordance
# time-dependent AUC:
perf$tdAUC
# }