# CalcCoxCalibP: 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 proportional hazards calibration model fit, and given covariates and collected data on the history of the binary exposure for each participant.

## Usage

 `1` ```CalcCoxCalibP(w, w.res, point, fit.cox, hz.times, Q) ```

## 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 `fit.cox` The result of `icenReg::ic_sp` on the interval-censored data `hz.times` Times used for calculating the baseline hazard function from PH calibration model `Q` Matrix of covariates for the PH calibration model

## Value

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```sim.data <- ICcalib:::SimCoxIntervalCensCox(n.sample = 200, lambda = 0.1, alpha = 0.25, beta0 = 0, gamma.q = c(log(0.75), log(2.5)), gamma.z = log(1.5), mu = 0.2, n.points = 2) # The baseline hazard for the calibration model is calculated in observation times cox.hz.times <- sort(unique(sim.data\$obs.tm)) # Fit proprtional hazards calibration model fit.cox <- FitCalibCox(w = sim.data\$w, w.res = sim.data\$w.res, Q = sim.data\$Q, hz.times = cox.hz.times, n.int = 5, order = 2) # Calculate the conditional probabilities of binary covariate=1 at time one probs <- CalcCoxCalibP(w = sim.data\$w, w.res = sim.data\$w.res, point = 1, Q = sim.data\$Q, fit.cox = fit.cox, hz.times = cox.hz.times) summary(probs) ```

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