rs.surv  R Documentation 
Computes an estimate of the relative survival curve using the Ederer I, Ederer II method, PoharPerme method or the Hakulinen method
rs.surv( formula = formula(data), data = parent.frame(), ratetable = relsurv::slopop, na.action, fin.date, method = "poharperme", conf.type = "log", conf.int = 0.95, type = "kaplanmeier", add.times, precision = 1, rmap )
formula 
a formula object, with the response as a NOTE: The followup time must be in days. 
data 
a data.frame in which to interpret the variables named in the

ratetable 
a table of event rates, organized as a 
na.action 
a missingdata filter function, applied to the model.frame,
after any subset argument has been used. Default is

fin.date 
the date of the study ending, used for calculating the
potential followup times in the Hakulinen method. If missing, it is
calculated as 
method 
the method for calculating the relative survival. The options
are 
conf.type 
one of 
conf.int 
the level for a twosided confidence interval on the survival curve(s). Default is 0.95. 
type 
defines how survival estimates are to be calculated given the
hazards. The default ( 
add.times 
specific times at which the curve should be evaluated. 
precision 
Precision for numerical integration. Default is 1, which means that daily intervals are taken, the value may be decreased to get a higher precision or increased to achieve a faster calculation. The calculation intervals always include at least all times of event and censoring as border points. 
rmap 
an optional list to be used if the variables are not organized
and named in the same way as in the 
NOTE: The followup time must be specified in days. The ratetable
being used may have different variable names and formats than the user's
data set, this is dealt with by the rmap
argument. For example, if
age is in years in the data set but in days in the ratetable
object,
age=age*365.241 should be used. The calendar year can be in any date format
(date, Date and POSIXt are allowed), the date formats in the
ratetable
and in the data may differ.
The potential censoring times needed for the calculation of the expected
survival by the Hakulinen method are calculated automatically. The times of
censoring are left as they are, the times of events are replaced with
fin.date  year
.
The calculation of the PoharPerme estimate is more time consuming since
more data are needed from the population tables. The old version of the
function, now named rs.survo
can be used as a faster version for the
Hakulinen and Ederer II estimate.
Numerical integration is required for PoharPerme estimate. The integration
precision is set with argument precision
, which defaults to daily
intervals, a default that should give enough precision for any practical
purpose.
Note that even though the estimate is always calculated using numerical
integration, only the values at event and censoring times are reported.
Hence, the function plot
draws a step function in between and the
function summary
reports the value at the last event or censoring
time before the specified time. If the output of the estimated values at
other points is required, this should be specified with argument
add.times
.
a survfit
object; see the help on survfit.object
for
details. The survfit
methods are used for print
,
summary
, plot
, lines
, and points
.
Package: Pohar Perme, M., Pavlic, K. (2018) "Nonparametric Relative Survival Analysis with the R Package relsurv". Journal of Statistical Software. 87(8), 127, doi: "10.18637/jss.v087.i08" Theory: Pohar Perme, M., Esteve, J., Rachet, B. (2016) "Analysing PopulationBased Cancer Survival  Settling the Controversies." BMC Cancer, 16 (933), 18. doi:10.1186/s1288501629679. Theory: Pohar Perme, M., Stare, J., Esteve, J. (2012) "On Estimation in Relative Survival", Biometrics, 68(1), 113120. doi:10.1111/j.15410420.2011.01640.x.
survfit
, survexp
data(slopop) data(rdata) #calculate the relative survival curve #note that the variable year must be given in a date format and that #age must be multiplied by 365.241 in order to be expressed in days. rs.surv(Surv(time,cens)~sex,rmap=list(age=age*365.241), ratetable=slopop,data=rdata)
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