performance_prc {pencal}

Predictive performance of the PRC-LMM and PRC-MLPMM models

Description

This function computes the naive and optimism-corrected measures of performance (C index, time-dependent AUC and time-dependent Brier score) for the PRC models proposed in Signorelli et al. (2021). The optimism correction is computed based on a cluster bootstrap optimism correction procedure (CBOCP)

Usage

performance_prc(step2, step3, metric = c("tdauc", "c", "brier"),
  times = c(2, 3), n.cores = 1, verbose = TRUE)

Arguments

step2

the output of either summarize_lmms or summarize_mlpmms (step 2 of the estimation of PRC)

step3

the output of fit_prclmm or fit_prcmlpmm (step 3 of PRC)

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

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;

• Brier: a data frame with the naive and optimism-corrected estimates of the time-dependent Brier score at the desired time points;

Author(s)

Mirko Signorelli

References

Signorelli, M. (2024). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. To appear in: The R Journal. Preprint: arXiv:2309.15600

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

See Also

for the PRC-LMM model: fit_lmms (step 1), summarize_lmms (step 2) and fit_prclmm (step 3); for the PRC-MLPMM model: fit_mlpmms (step 1), summarize_mlpmms (step 2) and fit_prcmlpmm (step 3).

Examples

data(fitted_prclmm)

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
}
                   
# compute the time-dependent AUC
perf = performance_prc(fitted_prclmm$step2, fitted_prclmm$step3,
             metric = 'tdauc', times = c(3, 3.5, 4), 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