survpred_prcmlpmm {pencal}

Compute the predicted survival probabilities obtained from the PRC models

Description

This function computes the predicted survival probabilities for the for the PRC-MLPMM(U) and PRC-MLPMM(U+B) models proposed in Signorelli et al. (2021)

Usage

survpred_prcmlpmm(step2, step3, times = 1)

Arguments

step2

the output of summarize_mlpmms (step 2 of the estimation of PRC-MLPMM)

step3

the output of fit_prcmlpmm (step 3 of the estimation of PRC-MLPMM)

times

numeric vector with the time points at which to estimate the time-dependent AUC

Value

A data frame with the predicted survival probabilities computed at the supplied time points

Author(s)

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), summarize_mlpmms (step 2) and fit_prcmlpmm (step 3).

Examples

data(fitted_prcmlpmm)
                   
# predict survival probabilities at times 3 to 6
surv.probs = survpred_prcmlpmm(fitted_prcmlpmm$step2, 
                 fitted_prcmlpmm$step3, times = 3:6)
ls(surv.probs)
head(surv.probs$predicted_survival)