md.survnp: Nonparametric analysis of disease recurrence with missing...

In missDeaths: Simulating and Analyzing Time to Event Data in the Presence of Population Mortality

Description

Estimates the Net and Event free survial using a is non-parametric approach that aims to correct all individuals using the unconditional survival time distribution obtained from the population mortality table.

The idea comes from realizing that the number of observed events in the data equals the number which would be observed in case of a complete data set, but the number of patients at risk does not. Hence, this method adjusts the observed number at risk to mimic the one we would get if the data was complete.

Usage

md.survnp(time, status, maxtime, D, ratetable, conf.int = 0.95)

Arguments

time	the time to event (number of days)
status	the status indicator, 0=right censored, 1=event at time
maxtime	maximum potential observation time (number of days). where status=0 equals time. where status=1 equals potential time of right censoring if no event would be observed.
D	demographic information compatible with ratetable, see md.D.
ratetable	a population mortality table, default is slopop
conf.int	desired coverage of the estimated confidence interval

Value

A list with components giving the estimates of net and event free survival.

time	times where the estimates are calculated (number of days)
Y.net	adjusted number of patients at risk at each time in a hypothetical world where patients don't die
Y.efs	adjusted number of patients at risk at each time
surv.net	the estimated Net survival
std.err.net	the estimated standard error of Net survival estimates
surv.efs	the estimated Event free survival
std.err.efs	the estimated standard error of Event free survival estimates

References

Stupnik T., Pohar Perme M. (2015) "Analysing disease recurrence with missing at risk information." Statistics in Medicine 35. p1130-43. https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.6766

Examples

## Not run: 
library(missDeaths)
library(cmprsk)
data(slopop)

data(observed)
D = md.D(age=observed$age*365.2425, sex=observed$sex, year=(observed$year - 1970)*365.2425)
np = md.survnp(observed$time*365.2425, observed$status, observed$maxtime*365.2425, D, slopop)

#calculate net survival at 3 and 9 years
w = list(list(time=np$time, est=np$surv.net, var=(np$std.err.net)^2))
timepoints(w, times=c(3,9)*365.2425)

#plot the net and event free survival curves
plot(np$time, np$surv.net)
plot(np$time, np$surv.efs)

## End(Not run)

missDeaths documentation built on Oct. 23, 2020, 6:39 p.m.