Description Usage Arguments Details Value References See Also Examples
The function fits an additive model to the data. The methods implemented are the maximum likelihood method, the semiparametric
method, a glm model with a binomial
error and a glm model with a poisson
error.
1 2 
formula 
a formula object, with the response on the left of a NOTE: The time must be in days, and the same
is required for the ratetable variables (the variables used in the population tables), for example age and year (year must be
given in the 
data 
a data.frame in which to interpret the variables named in
the 
ratetable 
a table of event rates, organized as a 
int 
either a single value denoting the number of followup years or a vector
specifying the intervals (in years) in which the hazard is constant (the times that are
bigger than 
na.action 
a missingdata filter function, applied to the model.frame,
after any subset argument has been used. Default is

method 

init 
vector of initial values of the iteration. Default initial value is zero for all variables. 
bwin 
controls the bandwidth used for smoothing in the EM algorithm. The followup time is divided into quartiles and

centered 
if 
cause 
A vector of the same length as the number of cases. 
control 
a list of parameters for controlling the fitting process.
See the documentation for 
... 
other arguments will be passed to 
NOTE: All times used in the formula argument must be specified in days. This is true for the followup time as well as for
any variables needed ratetable
object, like age
and year
. On the contrary, the int
argument requires
interval specification in years.
The maximum likelihood method and both glm methods assume a fully parametric model with a piecewise constant baseline
excess hazard function. The intervals on which the baseline is assumed constant should be passed via argument int
. The
EM method is semiparametric, i.e. no assumptions are made for the baseline hazard and therefore no intervals need to be specified.
The methods using glm are methods for grouped data. The groups are formed according to the covariate values.
This should be taken into account when fitting a model. The glm method returns life tables for groups specified by the covariates in groups
.
The EM method output includes the smoothed baseline excess hazard lambda0
, the cumulative baseline excess hazard
Lambda0
and times
at which they are estimated. The individual probabilites of dying due to the excess risk
are returned as Nie
.
The EM method fitting procedure requires some local smoothing of the baseline excess hazard. The default bwin=1
value lets the function find an appropriate value for the
smoothing band width. While this ensures an unbiased estimate, the procedure time is much longer. As the value found by
the function is independent of the covariates in the model, the value can be read from the output (bwinfac
) and
used for refitting different models to the same data to save time.
An object of class rsadd
. In the case of method="glm.bin"
and method="glm.poi"
the class also
inherits from glm
which inherits from the class lm
.
Objects of this class have methods for the functions print
and summary
.
An object of class rsadd
is a list containing at least the following components:
data 
the data as used in the model, along with the variables defined in the rate table 
ratetable 
the ratetable used. 
int 
the maximum time (in years) used. All the events at and after this value are censored. 
method 
the fitting method that was used. 
linear.predictors 
the vector of linear predictors, one per subject. 
Package. Pohar M., Stare J. (2006) "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, 81: 272–278
Relative survival: Pohar, M., Stare, J. (2007) "Making relative survival analysis relatively easy." Computers in biology and medicine, 37: 1741–1749.
EM algorithm: Pohar Perme M., Henderson R., Stare, J. (2009) "An approach to estimation in relative survival regression." Biostatistics, 10: 136–146.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  data(slopop)
data(rdata)
#fit an additive model
#note that the variable year is in the date format  the number of days since 01.01.1960 and that
#age must be multiplied by 365.241 in order to be expressed in days.
fit < rsadd(Surv(time,cens)~sex+as.factor(agegr)+ratetable(age=age*365.241,
sex=sex,year=year), ratetable=slopop,data=rdata,int=5)
#check the goodness of fit
rs.br(fit)
#use the EM method and plot the smoothed baseline excess hazard
fit < rsadd(Surv(time,cens)~sex+age+ratetable(age=age*365.241,
sex=sex,year=year), ratetable=slopop,data=rdata,int=5,method="EM")
sm < epa(fit)
plot(sm$times,sm$lambda,type="l")

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