In ampel-leipzig/ameld: Data and Model of End-Stage Liver Disease used in the AMPEL Project

Sys.setenv(LANGUAGE = "en")
library("ameld")

knitr::write_bib("glmnet", "rpackages.bib")

Authors: r packageDescription("ameld")[["Author"]]
Last modified: r file.info("ameld.Rmd")$mtime
Compiled: r date()

Introduction

The ameld R package extends glmnet::cv.glmnet [@R-glmnet; @glmnet2010]. It supports a repeated cross-validation (rcv.glmnet) and a repeated cross-validation to tune alpha and lambda simultaneously (arcv.glmnet). Additionally it provides a bootstrap function that could utilize both functions and supports survival data as described in @harrell1996.

Dataset

We use the eldd dataset provided by ameld (see ?eldd for details) and standardize it using the zlog [@hoffmann2017] method.

library("ameld")
library("zlog")
data(eldd)
data(eldr)

# transform reference data.frame for zlog
r <- eldr[c("Code", "AgeDays", "Sex", "LowerLimit", "UpperLimit")]
names(r) <- c("param", "age", "sex", "lower", "upper")
r$age <- r$age / 365.25
r <- set_missing_limits(r)

## we just want to standardize laboratory values
cn <- colnames(eldd)
cnlabs <- cn[grepl("_[SCEFQ1]$", cn)]
zeldd <- eldd
zeldd[c("Age", "Sex", cnlabs)] <- zlog_df(eldd[, c("Age", "Sex", cnlabs)], r)
zeldd[c("Age", "Sex", cnlabs)] <- impute_df(zeldd[c("Age", "Sex", cnlabs)], r)
zeldd <- na.omit(zeldd)

Bootstrapping

Next we apply the bootstrapping. In general the number of bootstrap samples nboot should be equal or larger than 100. We use a much smaller number here to keep the runtime low.

library("future")
srv <- Surv(zeldd$DaysAtRisk, zeldd$Deceased)
zeldd$DaysAtRisk <- zeldd$Deceased <- NULL
x <- data.matrix(zeldd)

bt <- bootstrap(
    x, srv,
    fun = rcv.glmnet,
    family = "cox",
    nboot = 3,
    nfolds = 3,
    nrep = 2
)

We could show an optimism corrected calibration curve.

plot(bt, what = "calibration")

Additionally we could see which variables are selected in each bootstrap step.

plot(bt, what = "selected")

Automatically select best alpha in each Bootstrapping Step.

It is possible to use arcv.glmnet to automatically select the best alpha in each bootstrap step.

selarcv <- function(...) {
    dots <- list(...)
    a <- arcv.glmnet(...)
    i <- which.min.error(a, s = dots$s, maxnnzero = dots$maxnnzero)
    a$models[[i]]
}

bt <- bootstrap(
    x, srv,
    fun = selarcv,
    family = "cox",
    alpha = seq(0, 1, len = 11)^3,
    s = "lambda.1se",
    maxnnzero = 9,
    nboot = 10L, nfolds = 3, nrep = 5,
    m = 50, times = 90
)

Acknowledgment

This work is part of the AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings) project.

This measure is co-funded with tax revenues based on the budget adopted by the members of the Saxon State Parliament.

Session Information

sessionInfo()

References

ampel-leipzig/ameld documentation built on Aug. 23, 2024, 7:31 p.m.