pencal (version 1.0.2)

simulate_prclmm_data: Simulate data that can be used to fit the PRC-LMM model

Description

This function allows to simulate a survival outcome from longitudinal predictors. Specifically, the longitudinal predictors are simulated from linear mixed models (LMMs), and the survival outcome from a Weibull model where the time to event depends on the random effects from the LMMs. It is an implementation of the simulation method used in Signorelli et al. (2021)

Usage

simulate_prclmm_data(n = 100, p = 10, p.relev = 4, lambda = 0.2,
  nu = 2, seed = 1, base.age.range = c(3, 5), cens.range = c(0.5, 10),
  t.values = c(0, 0.5, 1, 2))

Arguments

n

sample size

p

number of longitudinal outcomes

p.relev

number of longitudinal outcomes that are associated with the survival outcome (min: 1, max: p)

lambda

Weibull location parameter, positive

nu

Weibull scale parameter, positive

seed

random seed (defaults to 1)

base.age.range

range for age at baseline (set it equal to c(0, 0) if you want all subjects to enter the study at the same age)

cens.range

range for censoring times

t.values

vector specifying the time points at which longitudinal measurements are collected (NB: for simplicity, this function assumes a balanced designed; however, pencal is designed to work both with balanced and with unbalanced designs!)

Value

A list containing the following elements:

• a dataframe long.data with data on the longitudinal predictors, comprehensive of a subject id (id), baseline age (base.age), time from baseline (t.from.base) and the longitudinal biomarkers;

• a dataframe surv.data with the survival data: a subject id (id), baseline age (baseline.age), the time to event outcome (time) and a binary vector (event) that is 1 if the event is observed, and 0 in case of right-censoring;

• perc.cens the proportion of censored individuals in the simulated dataset.

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

# NOT RUN {
# generate example data
simdata = simulate_prclmm_data(n = 20, p = 10, 
                        p.relev = 4, seed = 1)
# view the longitudinal markers:
library(ptmixed)
make.spaghetti(x = age, y = marker1, 
               id = id, group = id,
               data = simdata$long.data, 
               legend.inset = - 1)
# proportion of censored subjects
simdata$censoring.prop
# visualize KM estimate of survival
library(survival)
surv.obj = Surv(time = simdata$surv.data$time, 
                event = simdata$surv.data$event)
kaplan <- survfit(surv.obj ~ 1,  
                  type="kaplan-meier")
plot(kaplan)
# }