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pencal (version 1.0.2)

Penalized Regression Calibration (PRC)

Description

Computes penalized regression calibration (PRC), a statistical method that allows to predict survival from high-dimensional longitudinal predictors. PRC is described in Signorelli et al. (2021, DOI: )).

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Version

Install

install.packages('pencal')

Monthly Downloads

243

Version

1.0.2

License

GPL-3

Maintainer

Mirko Signorelli

Last Published

February 14th, 2022

Functions in pencal (1.0.2)

fit_mlpmms

Step 1 of PRC-MLPMM (estimation of the linear mixed models)
fit_lmms

Step 1 of PRC-LMM (estimation of the linear mixed models)
draw_cluster_bootstrap

Draw a cluster bootstrap sample from a data frame in long format
fit_prclmm

Step 3 of PRC-LMM (estimation of the penalized Cox model(s))
summarize_lmms

Step 2 of PRC-LMM (computation of the predicted random effects)
summarize_mlpmms

Step 2 of PRC-MLPMM (computation of the predicted random effects)
performance_prc

Predictive performance of the PRC-LMM and PRC-MLPMM models
simulate_t_weibull

Generate survival data from a Weibull model
fit_prcmlpmm

Step 3 of PRC-MLPMM (estimation of the penalized Cox model(s))
survpred_prclmm

Compute the predicted survival probabilities obtained from the PRC models
performance_pencox_baseline

Predictive performance of the penalized Cox model with baseline covariates
fitted_prcmlpmm

A fitted PRC MLPMM
survpred_prcmlpmm

Compute the predicted survival probabilities obtained from the PRC models
simulate_prcmlpmm_data

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

Estimation of a penalized Cox model with baseline covariates onlu
fitted_prclmm

A fitted PRC LMM
prepare_longdata

Prepare longitudinal data for PRC
simulate_prclmm_data

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