survpred_prclmm {pencal}

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)

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

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.

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_lmms (step 1), summarize_lmms (step 2) and fit_prclmm (step 3)

Examples

# generate example data
set.seed(1234)
p = 4 # number of longitudinal predictors
simdata = simulate_prclmm_data(n = 100, p = p, p.relev = 2, 
             t.values = c(0, 0.2, 0.5, 1, 1.5, 2),
             landmark = 2, seed = 123)
             
# 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 3 to 6
surv.probs = survpred_prclmm(step1, step2, step3, times = 3:6)
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 = 3:6,
                     new.longdata = new.longdata,
                     new.basecovs = new.basecovs)
head(surv.probs.new$predicted_survival)