accum: Aggregate data from a simulated cohort.
In survsim: Simulation of Simple and Complex Survival Data

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/accum.R

Aggregate the observed number of events suffered by a subject, the time of follow-up, the duration of all the observed episodes and the real number of events suffered in all subject history.

1	accum(data)

data

An object of class mult.ev.data.sim, if the individual cohort has been simulated in a multiple event situation or an object of class rec.ev.data.sim, if the individual cohort has been simulated in a recurrent event situation. Note that, although the routine will work, it's probably not much useful in other contexts than recurrent event situation.

The output contains z and real.ep.accum because they can be interesting when analyzing several aspects as missing data or individual heterogeneity, although those variables cannot be observed in a real cohort.

An object of class sim.ev.agg.data. It is a data frame with a row for each subject in data, and the following columns

`nid`	an `integer` number that identifies the subject.
`old`	real value indicating the time that the individual was at risk before the beginning of the follow-up.
`risk.bef`	`Boolean` indicating if the subject was at risk before the beginning of the follow-up time or not.
`z`	individual heterogeneity, generated according to the specified distribution.
`x`	value of each covariate randomly generated for each subject in the cohort.
`obs.ep.accum`	aggregated number of episodes suffered by an individual since the beginning of subject's follow-up time.
`real.ep.accum`	aggregated number of episodes suffered by an individual from the beginning of subject's history.
`time.accum`	global time of follow-up for each individual.
`long.accum`	global time not at risk within the follow-up time, corresponding to the sum of times between the end of an event and the beginning of the next.

David Moriña, Universitat de Barcelona and Albert Navarro, Universitat Autònoma de Barcelona

Kelly PJ, Lim LL. Survival analysis for recurrent event data: an application to childhood infectious diseases. Stat Med 2000 Jan 15;19(1):13-33.

Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med 2005 Jun 15;24(11):1713-1723.

Metcalfe C, Thompson SG. The importance of varying the event generation process in simulation studies of statistical methods for recurrent events. Stat Med 2006 Jan 15;25(1):165-179.

Reis RJ, Utzet M, La Rocca PF, Nedel FB, Martin M, Navarro A. Previous sick leaves as predictor of subsequent ones. Int Arch Occup Environ Health 2011 Jun;84(5):491-499.

Navarro A, Moriña D, Reis R, Nedel FB, Martin M, Alvarado S. Hazard functions to describe patterns of new and recurrent sick leave episodes for different diagnoses. Scand J Work Environ Health 2012 Jan 27.

Moriña D, Navarro A. The R package survsim for the simulation of simple and complex survival data. Journal of Statistical Software 2014 Jul; 59(2):1-20.

rec.ev.sim, mult.ev.sim, crisk.sim, survsim, simple.surv.sim

### A cohort with 500 subjects, with a maximum follow-up time of 1825 days and
### just a covariate, following a Bernoulli distribution, and a corresponding
### beta of -0.4, -0.5, -0.6 and -0.7 for each episode, in a context of recurrent
### events.

sim.data <- rec.ev.sim(n=500, foltime=1825, dist.ev=c('lnorm','llogistic', 'weibull',
'weibull'),anc.ev=c(1.498, 0.924, 0.923, 1.051),beta0.ev=c(7.195, 6.583, 6.678, 6.430)
,,anc.cens=c(1.272, 1.218, 1.341, 1.484),beta0.cens=c(7.315, 6.975, 6.712, 6.399), 
z=list(c("unif",0.8,1.2)),beta=list(c(-0.4,-0.5,-0.6,-0.7)), x=list(c("bern", 0.5)),
lambda=c(2.18,2.33,2.40,3.46),priskb=0.5,max.old=730)

### Aggregated data

accum.data   <- accum(sim.data)

head(accum.data)