estRecal: Logistic Recalibration Based On Linear Predictors

In discSurv: Discrete Time Survival Analysis

Logistic Recalibration Based On Linear Predictors

Description

Fits a logistic recalibration model to independent test data. It updates the intercept and slope parameters. It is assumed that the factor levels of time are equal in both training and validation data. Time dependent covariates, discrete cause specific competing risks and subdistribution hazards are also supported.

Usage

estRecal(testLinPred, testDataLong, weights = NULL)

Arguments

testLinPred	Calculated linear predictor on the validation data with model fitted on training data (class "numeric").
testDataLong	Validation data set in long format (class "data.frame").
weights	Weights used in estimation of the logistic recalibration model (class "numeric"). Default is no weighting (NULL).

Details

Three statistical tests for recalibration are computed. The coefficient alpha is the intercept and beta is the slope parameter of the recalibration model.

• Test1 Tests the overall reliability of predictions with null hypothesis \alpha=0 and \beta=0.

• Test2 Tests incorrect calibration given appropriate refinement with null hypothesis \alpha=0 given \beta=1.

• Test3 Tests refinement given corrected calibration with null hypothesis \beta=1 given estimated \alpha.

In case of competing risks the test assume an extendend null hypothesis that includes all events for each test type.

Value

Continuation ratio model that calibrates estimated discrete hazards to new validation environment (class c("glm", "lm")).

Note

Updates estimated hazards of discrete survival models to better adapt to a different environment. If there are substantial environment changes the predicted probabilities will differ between two environments. Logistic recalibration may be used to improve the calibration of predicted probabilities by incorperating information from the existing model and data from the environment. This approach works for any survival prediction model with one event that provides linear predictors.

Author(s)

Thomas Welchowski [e-mail]

References

\insertRef

heyardValCompRisksdiscSurv

\insertRefvalidationProbdiscSurv

See Also

dataLong, dataLongTimeDep, dataLongCompRisks, dataLongCompRisksTimeDep, dataLongSubDist, calPlot

Examples

####################
# Data preprocessing

# Example unemployment data
library(Ecdat)
data(UnempDur)

# Select subsample
selectInd1 <- 1:100
selectInd2 <- 101:200
trainSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd1], ]
valSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd2], ]

####################
# One event

# Convert to long format
trainSet_long <- dataLong(dataShort = trainSet, timeColumn = "spell", eventColumn = "censor1")
valSet_long <- dataLong(dataShort = valSet, timeColumn = "spell", eventColumn = "censor1")

# Estimate continuation ratio model with logit link
glmFit <- glm(formula = y ~ timeInt + age + logwage, data = trainSet_long, family = binomial())

# Calculate linear predictors on validation set
linPred <- predict(glmFit, newdata = valSet_long, type = "link")

# Estimate logistic recalibration model
recalModel <- estRecal(testLinPred = linPred, testDataLong = valSet_long)
summary(recalModel)

# Calibration plots
hazOrg <- predict(glmFit, newdata = valSet_long, type = "response")
hazRecal <- predict(recalModel, newdata = data.frame(linPred), type = "response")

# Before logistic recalibration
calPlot(hazOrg, testDataLong = valSet_long)
# After logistic recalibration
calPlot(hazRecal, testDataLong = valSet_long)

############################
# Two cause specific hazards 
library(VGAM)

# Convert to long format
trainSet_long <- dataLongCompRisks(dataShort = trainSet, timeColumn = "spell", 
eventColumns = c("censor1", "censor4"))
valSet_long <- dataLongCompRisks(dataShort = valSet, timeColumn = "spell", 
eventColumns = c("censor1", "censor4"))

# Estimate continuation ratio model with logit link
vglmFit <- VGAM::vglm(formula = cbind(e0, e1, e2) ~ timeInt + age + logwage, data = trainSet_long, 
family = VGAM::multinomial(refLevel = "e0"))

# Calculate linear predictors on training and test set
linPred <- VGAM::predictvglm(vglmFit, newdata = valSet_long, type = "link")

# Estimate logistic recalibration model
recalModel <- estRecal(testLinPred = linPred, testDataLong = valSet_long)
recalModel

# Calibration plots
hazOrg <- predict(vglmFit, newdata = valSet_long, type = "response")
predDat <- as.data.frame(linPred)
names(predDat) <- recalModel@misc$colnames.x[-1]
hazRecal <- predictvglm(recalModel, newdata = predDat, type = "response")

# Before logistic recalibration
calPlot(hazOrg, testDataLong = valSet_long, event = "e1")
calPlot(hazOrg, testDataLong = valSet_long, event = "e2")
# After logistic recalibration
calPlot(hazRecal, testDataLong = valSet_long, event = "e1")
calPlot(hazRecal, testDataLong = valSet_long, event = "e2")

###############################
# Subdistribution hazards model

# Convert to long format
trainSet_long <- dataLongSubDist(dataShort = trainSet, timeColumn = "spell", 
eventColumns = c("censor1", "censor4"), eventFocus = "censor1")
valSet_long <- dataLongSubDist(dataShort = valSet, timeColumn = "spell", 
eventColumns = c("censor1", "censor4"), eventFocus = "censor1")

# Estimate continuation ratio model with logit link
glmFit <- glm(formula = y ~ timeInt + age + logwage, data = trainSet_long, 
family = binomial(), weights = trainSet_long$subDistWeights)

# Calculate linear predictors on training and test set
linPred <- predict(glmFit, newdata = valSet_long, type = "link")

# Estimate logistic recalibration model
recalModel <- estRecal(testLinPred = linPred, testDataLong = valSet_long, 
weights = valSet_long$subDistWeights)
recalModel

# Calibration plots
hazOrg <- predict(glmFit, newdata = valSet_long, type = "response",
weights = valSet_long$subDistWeights)
hazRecal <- predict(recalModel, newdata = data.frame(linPred), type = "response",
weights = valSet_long$subDistWeights)

# Before logistic recalibration
calPlot(hazOrg, testDataLong = valSet_long,
weights=valSet_long$subDistWeights)
# After logistic recalibration
calPlot(hazRecal, testDataLong = valSet_long,
weights=valSet_long$subDistWeights)

discSurv documentation built on April 29, 2026, 9:07 a.m.