CalcWeibullCalibP: Calculating the probabilities of positive binary exposure...

In ICcalib: Cox Model with Interval-Censored Starting Time of a Covariate

Description

For a given time point, calculate the probability of positive exposure value for multiple observations (participants). The function uses the results of a Weibull calibration model fit, and given collected data on the history of the binary exposure for each participant.

Usage

CalcWeibullCalibP(w, w.res, point, weib.params)

Arguments

w	A matrix of time points when measurements on the binary covariate were obtained.
w.res	A matrix of measurement results of the binary covariate. Each measurement corresponds to the time points in w
point	The time point at which the probabilities are estimated.
weib.params	A bivariate vector. Shape and scale parameters of the Weibull calibration model.

Value

A vector of estimated probabilities of positive exposure status at time point.

See Also

Weibull

Examples

# Simulate data set
sim.data <- ICcalib:::SimCoxIntervalCensSingle(n.sample = 200, lambda = 0.1, 
                                               alpha = 0.25, beta0 = log(0.5), 
                                               mu = 0.2, n.points = 2, 
                                               weib.shape = 1, weib.scale = 2)
# Fit a Weibull calibration model for the covariate starting time distribution
calib.weib.params <- FitCalibWeibull(w = sim.data$w, w.res = sim.data$w.res)
# Calculate the conditional probabilities of binary covariate=1 at time one
probs <- CalcWeibullCalibP(w = sim.data$w, w.res = sim.data$w.res, point = 1,
                           weib.params = calib.weib.params)
summary(probs)

ICcalib documentation built on May 2, 2019, 3:04 a.m.