survpred_prclmm: Compute the predicted survival probabilities obtained from the PRC models

Description

This function computes the predicted survival probabilities for the for the PRC-LMM model proposed in Signorelli et al. (2021)

Usage

survpred_prclmm(step1, step2, step3, times = 1, new.longdata = NULL,
  new.basecovs = NULL, keep.ranef = FALSE)

Value

A list containing the function call (call), a data frame with the predicted survival probabilities computed at the supplied time points (predicted_survival), and if keep.ranef = TRUE also the predicted random effects predicted_ranefs.

Arguments

step1: the output of fit_lmms (step 1 of the estimation of PRC-LMM)
step2: the output of summarize_lmms (step 2 of the estimation of PRC-LMM)
step3: the output of fit_prclmm (step 3 of the estimation of PRC-LMM)
times: numeric vector with the time points at which to estimate the time-dependent AUC
new.longdata: longitudinal data if you want to compute predictions for new subjects on which the model was not trained. It should comprise an identifier variable called `id`. Default is new.longdata = NULL
new.basecovs: a dataframe with baseline covariates for the new subjects for which predictions are to be computed. It should comprise an identifier variable called `id`. Only needed if baseline covariates were included in step 3 and new.longdata is specified. Default is new.basecovs = NULL
keep.ranef: should a data frame with the predicted random effects be included in the output? Default is FALSE

Author

Mirko Signorelli

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

# 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))
             
# step 1 of PRC-LMM: estimate the LMMs
y.names = paste('marker', 1:p, sep = '')
step1 = fit_lmms(y.names = y.names, 
                 fixefs = ~ age, ranefs = ~ age | id, 
                 long.data = simdata$long.data, 
                 surv.data = simdata$surv.data,
                 t.from.base = t.from.base,
                 n.boots = 0)
                 
# step 2 of PRC-LMM: compute the summaries 
# of the longitudinal outcomes
step2 = summarize_lmms(object = step1)

# step 3 of PRC-LMM: fit the penalized Cox models
step3 = fit_prclmm(object = step2, surv.data = simdata$surv.data,
                   baseline.covs = ~ baseline.age,
                   penalty = 'ridge')
                   
# predict survival probabilities at times 1, 2, 3
surv.probs = survpred_prclmm(step1, step2, step3, times = 1:3)
head(surv.probs$predicted_survival)

# predict survival probabilities for new subjects:
temp = simulate_prclmm_data(n = 10, p = p, p.relev = 2, 
      seed = 321, t.values = c(0, 0.2, 0.5, 1, 1.5, 2))
new.longdata = temp$long.data
new.basecovs = temp$surv.data[ , 1:2]
surv.probs.new = survpred_prclmm(step1, step2, step3, 
                     times = 1:3,
                     new.longdata = new.longdata,
                     new.basecovs = new.basecovs)
head(surv.probs.new$predicted_survival)

Run the code above in your browser using DataLab

State of Data and AI Literacy Report 2025