casebase (version 0.1.0)

fitSmoothHazard: Fit smooth-in-time parametric hazard functions.

Description

Miettinen and Hanley (2009) explained how case-base sampling can be used to estimate smooth-in-time parametric hazard functions. The idea is to sample person-moments, which may or may not correspond to an event, and then fit the hazard using logistic regression.

Usage

fitSmoothHazard(formula, data, time, censored.indicator, ...)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under Details.

data

a data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which fitSmoothHazard is called.

time

a character string giving the name of the time variable. See Details.

censored.indicator

a character string of length 1 indicating which value in event is the censored. This function will use relevel to set censored.indicator as the reference level. This argument is ignored if the event variable is a numeric

...

Additional parameters passed to sampleCaseBase. If data inherits from the class cbData, then these parameters are ignored.

Value

An object of glm and lm when there is only one event of interest, or of class CompRisk, which inherits from vglm, for a competing risk analysis. As such, functions like summary, deviance and coefficients give familiar results.

Details

The object data should either be the output of the function sampleCaseBase or the source dataset on which case-base sampling will be performed. In the latter case, it is assumed that data contains the two columns corresponding to the supplied time and event variables. If time is missing, the function looks for a column named "time" in the data. Note that the event variable is inferred from formula, since it is the left hand side.

Examples

# NOT RUN {
# Simulate censored survival data for two outcome types from exponential distributions
library(data.table)
set.seed(12345)
nobs <- 5000
tlim <- 20

# simulation parameters
b1 <- 200
b2 <- 50

# event type 0-censored, 1-event of interest, 2-competing event
# t observed time/endpoint
# z is a binary covariate
DT <- data.table(z=rbinom(nobs, 1, 0.5))
DT[,`:=` ("t_event" = rweibull(nobs, 1, b1),
          "t_comp" = rweibull(nobs, 1, b2))]
DT[,`:=`("event" = 1 * (t_event < t_comp) + 2 * (t_event >= t_comp),
         "time" = pmin(t_event, t_comp))]
DT[time >= tlim, `:=`("event" = 0, "time" = tlim)]

out_linear <- fitSmoothHazard(event ~ time + z, DT)
out_log <- fitSmoothHazard(event ~ log(time) + z, DT)
# }