Icens | R Documentation |
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.
Icens(first.well, last.well, first.ill,
formula, model.type = c("MRR", "AER"), breaks,
boot = FALSE, alpha = 0.05, keep.sample = FALSE,
data)
## S3 method for class 'Icens'
summary(object, scale = 1, ...)
## S3 method for class 'Icens'
print(x, scale = 1, digits = 4, ...)
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. |
object |
an |
x |
an |
scale |
scaling factor for rates. |
digits |
how many digits is used for printing results. |
... |
Other parameters passed on. |
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 log-link,
estimating the baseline rates, and the excess risk if |
cov |
A glm object from a binomial model with complementary
log-log link, estimating the log-rate-ratios. 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, martyn.plummer@r-project.org, 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):2177-2189, 1996.
CP Farrington: Interval censored survival data: a generalized linear modelling approach. Statistics in Medicine, 15(3):283-292, 1996.
fit.add
fit.mult
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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