survrec (version 1.2-2)

psh.fit: Survival function estimator for recurrence time data using the estimator developed by Pea, Strawderman and Hollander

Description

Estimation of survival function for recurrence time data by means the generalized product limit estimator (PLE) method developed by Pe a, Strawderman and Hollander. The resulting object of class "survfitr" is plotted by `plot.survfitr', before it is returned.

Usage

psh.fit(x,tvals)

Arguments

x

a survival recurrent event object

tvals

vector of times where the survival function can be estimated.

Value

n

number of unit or subjects observed.

m

vector of number of recurrences in each subject (length n).

failed

vector of number of recurrences in each subject (length n*m). Vector ordered (e.g. times of first unit, times of second unit, ..., times of n-unit)

censored

vector of times of censorship for each subject (length n).

numdistinct

number of distinct failures times

distinct

vector of distinct failures times

AtRisk

matrix of number of persons-at-risk at each distinct time and for each subject

survfunc

vector of survival estimated in distinct times

tvals

copy of argument

PSHpleAttvals

vector of survival estimated in tvals times

Details

The estimator computed by this object is the nonparametric estimator of the inter-event time survivor function under the assumption of a renewal or IID model. This generalizes the product-limit estimator to the situation where the event is recurrent. For details and the theory behind this estimator, please refer to Pe\~na, Strawderman and Hollander (2001, JASA).

References

Pea, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. J. Amer. Statist. Assoc 96, 1299-1315.

See Also

survfitr Survr

Examples

data(MMC)
fit<-psh.fit(Survr(MMC$id,MMC$time,MMC$event))
fit
plot(fit,conf.int=FALSE)

# compare with MLE Frailty

fit<-mlefrailty.fit(Survr(MMC$id,MMC$time,MMC$event))
fit
lines(fit,lty=2)

# and with wang-chang

fit<-wc.fit(Survr(MMC$id,MMC$time,MMC$event))
fit
lines(fit,lty=3)