CalcCoxCalibRSIntsP: Calculating the probabilities of positive binary exposure... In daniel258/CoxBinChange: 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 proportional hazards grouped risk-set calibration model fit, and given covariates and collected data on the history of the binary exposure for each participant.

Usage

 `1` ```CalcCoxCalibRSIntsP(w, w.res, point, fit.cox.rs.ints, hz.times, Q, pts.for.ints) ```

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.rs.ints` The result of `FitCalibCoxRSInts` 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 `pts.for.ints` Points defining the intervals for grouping risk-sets (first one has to be zero). Should be sorted from zero up

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 15 16 17 18 19``` ```set.seed(17) sim.data <- ICcalib:::SimCoxIntervalCensCox(n.sample = 100, 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.rs.ints <- FitCalibCoxRSInts(w = sim.data\$w, w.res = sim.data\$w.res, Q = sim.data\$Q, hz.times = cox.hz.times, n.int = 5, order = 2, pts.for.ints = seq(0,4,1), tm = sim.data\$obs.tm, event = sim.data\$delta) # Calculate the conditional probabilities of binary covariate=1 at time one probs <- CalcCoxCalibRSIntsP(w = sim.data\$w, w.res = sim.data\$w.res, point = 1, fit.cox.rs.ints = fit.cox.rs.ints, pts.for.ints = seq(0,4,1), Q = sim.data\$Q, hz.times = cox.hz.times) summary(probs) ```

daniel258/CoxBinChange documentation built on Aug. 7, 2018, 4:10 p.m.