The models fitted assumes a piecewise constant baseline rate in
intervals specified by the argument breaks
, and for the
covariates either a multiplicative relative risk function (default) or
an additive excess risk function.
1 2 3 4 5 
first.well 
Time of entry to the study, i.e. the time first seen without event. Numerical vector. 
last.well 
Time last seen without event. Numerical vector. 
first.ill 
Time first seen with event. Numerical vector. 
formula 
Model formula for the log relative risk. 
model.type 
Which model should be fitted. 
breaks 
Breakpoints between intervals in which the underlying
timescale is assumed constant. Any observation outside the range of

boot 
Should bootstrap be performed to produce confidence intervals for parameters. If a number is given this will be the number of bootsrap samples. The default is 1000. 
alpha 
1 minus the confidence level. 
keep.sample 
Should the bootstrap sample of the parameter values be returned? 
data 
Data frame in which the times and formula are interpreted. 
The model is fitted by calling either fit.mult
or
fit.add
.
An object of class "Icens"
: a list with three components:
rates 
A glm object from a binomial model with loglink,
estimating the baseline rates, and the excess risk if 
cov 
A glm object from a binomial model with complementary
loglog link, estimating the lograteratios. Only if 
niter 
Nuber of iterations, a scalar 
boot.ci 
If 
sample 
A matrix of the parameterestimates from the bootstrapping. Rows refer to parameters, columns to bootstrap samples. 
Martyn Plummer, plummer@iarc.fr, Bendix Carstensen, b@bxc.dk
B Carstensen: Regression models for interval censored survival data: application to HIV infection in Danish homosexual men. Statistics in Medicine, 15(20):21772189, 1996.
CP Farrington: Interval censored survival data: a generalized linear modelling approach. Statistics in Medicine, 15(3):283292, 1996.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  data( hivDK )
# Convert the dates to fractional years so that rates are
# expressed in cases per year
for( i in 2:4 ) hivDK[,i] < cal.yr( hivDK[,i] )
m.RR < Icens( entry, well, ill,
model="MRR", formula=~pyr+us, breaks=seq(1980,1990,5),
data=hivDK)
# Currently the MRR model returns a list with 2 glm objects.
round( ci.lin( m.RR$rates ), 4 )
round( ci.lin( m.RR$cov, Exp=TRUE ), 4 )
# There is actually a print method:
print( m.RR )
m.ER < Icens( entry, well, ill,
model="AER", formula=~pyr+us, breaks=seq(1980,1990,5),
data=hivDK)
# There is actually a print method:
print( m.ER )

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