Description Usage Arguments Details Value See Also Examples
View source: R/FitCalibCoxRSInts.R
FitCalibCoxRS
fits proportional hazards riskset calibration models for timetoexposure from intervalcensored data with covariates. The exposure is a binary covariate measured
in intermittent times. The covariates (Q
) are associated with the timetoexposure. This function fits a calibration model at each main event time point,
using only members of the risk set at that time point.
model is fitted (for all the data) and used for that time point.
FitCalibCoxRSInts
fits proportional hazards grouped riskset calibration models for timetoexposure from intervalcensored data with covariates. The exposure is a binary covariate measured
in intermittent times. The covariates (Q
) are associated with the timetoexposure. Unlike FitCalibCoxRS
, this function fits a calibration model
at each of the given points for pts.for.ints
.
1 2 3 4  FitCalibCoxRS(w, w.res, Q, hz.times, tm, n.int = 5, order = 2, event)
FitCalibCoxRSInts(w, w.res, Q, hz.times, n.int = 5, order = 2, tm, event,
pts.for.ints)

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. It corresponds to the time points in 
Q 
Matrix of covariates for PH calibration model 
hz.times 
Times used for calculating the baseline hazard function of a PH calibration model 
tm 
Vector of observed main event time or censoring time 
n.int 
The number of interior knots to be used, see 
order 
the order of the basis functions. See 
event 
Vector of censoring indicators. 
pts.for.ints 
Points defining the intervals for grouping risksets (first one has to be zero). Should be sorted from zero up.

In case of an error in the modelfitting at a certain time point, a proportional hazards calibration model (for all the data) is fitted and used for that time point.
A list of Cox PH model fits, each supplemented with the knots and order used for the Isplines.
fast.PH.ICsurv.EM
, FitCalibCox
fast.PH.ICsurv.EM
, FitCalibCox
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  set.seed(2)
sim.data < ICcalib:::SimCoxIntervalCensCox(n.sample = 50, lambda = 0.1,
alpha = 0.25, beta0 = log(0.2),
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$obs.tm))
# Fit proprtional hazards grouped risksets calibration models
calib.ph.rs.fit < FitCalibCoxRSInts(w = sim.data$w, w.res = sim.data$w.res, Q = sim.data$Q,
hz.times = cox.hz.times, tm = sim.data$obs.tm,
event = sim.data$delta, pts.for.ints = seq(0, 3, 1.5),
n.int = 5, order = 2)
# Below is a more time consuming option (no grouping of risksets)
# FitCalibCoxRS(w = sim.data$w, w.res = sim.data$w.res, Q = sim.data$Q,
# hz.times = cox.hz.times, obs.tm = sim.data$obs.tm,
# event = sim.data$delta, n.int = 5, order = 1)

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