md.survcox: Fit a proportional hazards regression model over disease...

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

Fit a proportional hazards regression model over disease recurrence data with missing information of possible deaths

Description

An iterative approach is used in this method to estimate the conditional distribution required to correctly impute the times of deaths using population mortality tables.

Note, that simply imputing expected survival times may seem intuitive, but does not give unbiased estimates, since the right censored individuals are not a random subsample of the patients.

Usage

md.survcox(data, f, maxtime, D, ratetable, iterations = 4, R = 50)

Arguments

data	a data.frame in which to interpret the variables named in the formula.
f	a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
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
iterations	the number of iteration steps to be performed, default is 4
R	the number of multiple imputations performed to adjust the estimated variance of estimates, default is 50.

Value

if R equals 1 then an object of class coxph.object representing the fit.

if R > 1 then the result of the MIcombine of the coxph objects.

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

See Also

md.impute, MIcombine

Examples

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

data(observed)
observed$time = observed$time*365.2425
D = md.D(age=observed$age*365.2425, sex=observed$sex, year=(observed$year - 1970)*365.2425)

#fit a cox model (NOTE: estimated std error is slightly underestimated!)
md.survcox(observed, Surv(time, status) ~ age + sex + iq + elevation, 
  observed$maxtime*365.2425, D, slopop, iterations=4, R=1)

#multiple imputations to correct the stimated std error
md.survcox(observed, Surv(time, status) ~ age + sex + iq + elevation, 
  observed$maxtime*365.2425, D, slopop, iterations=4, R=50)

## End(Not run)

missDeaths documentation built on Oct. 7, 2024, 1:25 a.m.