missDeaths (version 2.7)

missDeaths: Simulating and analyzing time to event data in the presence of population mortality

Description

In analysis of time to event data we may have a situation where we know that a certain non-negligible competing risk exists, but is not recorded in the data. Due to competing nature of the risks, ignoring such a risk may significantly impact the at-risk group and thus lead to biased estimates. This problem can be found in several national registries of benign diseases, medical device implantations (e.g. hip, knee or heart pacemaker) etc. where law obliges physicians to report events whereas the information on patient deaths is unavailable; it is hence unclear how many devices are still in use at a given time. Under the assumption that the survival of an individual is not influenced by the event under study, general population mortality tables can be used to obtain unbiased estimates of the measures of interest or to verify the assumption that the bias introduced by the non-recorded deaths is truly negligible. Two approaches are implemented in the missdeaths package: - an iterative imputation method md.survcox and - a mortality adjusted at risk function md.survnp. The package also includes a comprehensive set of functions to simulate data that can be used for better understanding of these methods (See md.simulate).

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)
ratetable = survexp.us

sim = md.simparams() +
          md.sex("sex", 0.5) + 
          md.binom("Z1", 0.5) +
          md.mvnorm(c("Z2", "Z3"), c(100, 0), matrix(c(225, 3, 2, 1), 2, 2)) +
          md.sample("Z4", c(1, 2, 3, 4), c(0.25, 0.25, 0.25, 0.25)) +
          md.uniform("birth", as.Date("1925-1-1"), as.Date("1950-1-1")) +
          md.uniform("start", as.Date("2000-1-1"), as.Date("2005-1-1")) +
          md.death("death", ratetable, "sex", "birth", "start") +
          md.eval("age", "as.numeric(start - birth)/365.2425", 80, FALSE) + 
          md.exp("event", "start", c("age", "sex", "Z1", "Z2"), 
             c(0.1, 2, 1, 0.01), 0.05/365.2425)
data = md.simulate(sim, 1000)
          
#construct a complete right censored data set
complete = md.censor(data, "start", as.Date("2010-1-1"), c("event", "death"))

#construct an observed right censored data set with non-reported deaths
observed = complete
observed$time[which(complete$status == 2)] = observed$maxtime[which(complete$status == 2)]
observed$status[which(complete$status == 2)] = 0

#estimate coefficients of the proportional hazards model
D = md.D(age=observed$age, sex=observed$sex, year=observed$start)
md.survcox(observed, Surv(time, status) ~ age + sex + Z1 + Z2, 
          observed$maxtime, D, ratetable, iterations=4, R=50)
          
#estimate net- and event-free survival
np = md.survnp(observed$time, observed$status, observed$maxtime, D, ratetable)
w = list(list(time=np$time, est=np$surv.net, var=(np$std.err.net)^2))
timepoints(w, times=c(3,9)*365.2425)
plot(np$time/365.2425, np$surv.net)
plot(np$time/365.2425, np$surv.efs)
# }

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