summarize_mlpmms {pencal}

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)

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

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);

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

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

Examples

# 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)
summary(step2)