rsadd {relsurv} | R Documentation |
The function fits an additive model to the data. The methods implemented are the maximum likelihood method,
a glm model with a binomial
error and a glm model with a poisson
error.
rsadd(formula, data=parent.frame(), ratetable = survexp.us, int, na.action, method, init,bwin,centered,cause,control,...)
formula |
a formula object, with the response on the left of a ~ operator, and
the terms on the right. The terms consist of predictor variables separated by
the + operator, along with a ratetable term. The ratetable term
matches each subject to his/her expected cohort. If the variables are organized and named
in the same way as in the population tables, the ratetable term can be omitted.
The response must be a survival object as returned by the Surv function. The time must be in days.
|
data |
a data.frame in which to interpret the variables named in
the formula .
|
ratetable |
a table of event rates, organized as a ratetable object, such as survexp.us .
|
int |
either a single value denoting the number of follow-up years or a vector
specifying the intervals (in years) in which the hazard is constant (the times that are
bigger than max(int) are censored. If missing, the intervals are set to be one year
long and include the maximum observed follow-up time. The EM method does not need the intervals,
only the maximum time can be specified.
|
na.action |
a missing-data filter function, applied to the model.frame,
after any subset argument has been used. Default is
options()$na.action . |
method |
glm.bin or glm.poi for a glm model, EM for the EM algorithm and max.lik for the maximum likelihood model (default). |
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 follow-up time is divided into quartiles and
bwin specifies a factor by which the maximum between events time length on each interval is multiplied. The default
bwin=-1 lets the function find an appropriate value. If bwin=0 , no smoothing is applied.
|
centered |
if TRUE , all the variables are centered before fitting and the baseline excess hazard
is calculated accordingly. Default is FALSE . |
cause |
A vector of the same length as the number of cases. 0 for population deaths, 1 for disease specific
deaths, 2 (default) for unknown. Can only be used with the EM method. |
control |
a list of parameters for controlling the fitting process.
See the documentation for glm.control for details.
|
... |
other arguments will be passed to glm.control . |
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 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. |
Pohar M., Stare J. "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, 81: 272-278, 2006.
data(slopop) data(rdata) rsadd(Surv(time,cens)~sex+as.factor(agegr)+ratetable(age=age*365, sex=sex,year=year), ratetable=slopop,data=rdata,int=5) #use the EM method and plot the smoothed baseline excess hazard fit <- rsadd(Surv(time,cens)~sex+age+ratetable(age=age*365, sex=sex,year=year), ratetable=slopop,data=rdata,int=5,method="EM") plot(lowess(fit$times,fit$lambda0,f=.2),type="l")