pencal (version 2.1.1)

summarize_mlpmms: Step 2 of PRC-MLPMM (computation of the predicted random effects)

Description

This function performs the second step for the estimation of the PRC-MLPMM model proposed in Signorelli et al. (2021)

Usage

summarize_mlpmms(object, n.cores = 1, verbose = TRUE)

Value

A list containing the following objects:

• call: the function call

• ranef.orig: a matrix with the predicted random effects computed for the original data;

• n.boots: number of bootstrap samples;

• boot.ids: a list with the ids of bootstrapped subjects (when n.boots > 0);

• ranef.boot.train: a list where each element is a matrix that contains the predicted random effects for each bootstrap sample (when n.boots > 0);

• ranef.boot.valid: a list where each element is a matrix that contains the predicted random effects on the original data, based on the mlpmms fitted on the cluster bootstrap samples (when n.boots > 0);

Arguments

object

a list of objects as produced by fit_mlpmms

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. (2023). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. arXiv 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

fit_mlpmms (step 1), fit_prcmlpmm (step 3), performance_prc

Examples

# \donttest{
# generate example data
set.seed(123)
n.items = c(4,2,2,3,4,2)
simdata = simulate_prcmlpmm_data(n = 100, p = length(n.items),  
             p.relev = 3, n.items = n.items, 
             type = 'u+b', seed = 1)
 
# specify options for cluster bootstrap optimism correction
# procedure and for parallel computing 
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
}

# step 1 of PRC-MLPMM: estimate the MLPMMs
y.names = vector('list', length(n.items))
for (i in 1:length(n.items)) {
  y.names[[i]] = paste('marker', i, '_', 1:n.items[i], sep = '')
}

step1 = fit_mlpmms(y.names, fixefs = ~ contrast(age),  
                 ranef.time = age, randint.items = TRUE, 
                 long.data = simdata$long.data, 
                 surv.data = simdata$surv.data,
                 t.from.base = t.from.base,
                 n.boots = n.boots, n.cores = n.cores)

# step 2 of PRC-MLPMM: compute the summaries 
step2 = summarize_mlpmms(object = step1, n.cores = n.cores)
# }