estSurvCompRisks {discSurv}

Estimated Survival Function for Competing Risks

Description

Computes the survival function S(T>t|x) based on estimated hazards of a competing risks model. The discrete hazards may or may not depend on covariates. The covariates have to be equal across all estimated hazards. Therefore the given discrete hazards should only vary over time.

Usage

estSurvCompRisks(hazards)

Arguments

hazards

Estimated discrete hazards ("numeric matrix"). Discrete hazards of each time interval are stored in the rows and the number of columns equal to the number of events.

Details

The argument hazards must be given for all intervals [a_0, a_1), [a_1, a_2), ..., [a_q-1, a_q), [a_q, Inf).

Value

Estimated survival probabilities ("numeric vector")

Note

It is assumed that all time points up to the last interval [a_q, Inf) are available. If not already present, these can be added manually.

Author(s)

Moritz Berger [e-mail]
https://www.imbie.uni-bonn.de/personen/dr-moritz-berger/

References

Tutz G, Schmid M (2016). Modeling discrete time-to-event data. Springer Series in Statistics.

See Also

estSurv

Examples

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

# Select subsample
subUnempDur <- UnempDur [1:100, ]

# Convert to long format
UnempLong <- dataLongCompRisks(dataShort = subUnempDur, timeColumn = "spell", 
eventColumns = c("censor1", "censor4"))
head(UnempLong)

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

# Estimate discrete survival function given age, logwage of first person
hazards <- VGAM::predictvglm(vglmFit, newdata = subset(UnempLong, obj == 1), type = "response")[,-1]
SurvivalFuncCondX <- estSurvCompRisks(rbind(hazards, 0.5))
SurvivalFuncCondX