Compute a Survival Curve for Recurrent Event Data given a covariate

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Description

Computes an estimate of a survival curve for recurrent event data using either the Pe<f1>a-Strawderman-Hollander, Wang-Chang or MLE Frailty estimators. It also computes the asymptotic standard errors. The resulting object of class "survfitr" is plotted by ‘plot.survfitr’, before it is returned.

Usage

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survfitr(formula, data, type="MLEfrailty",...) 

Arguments

formula

A formula object. If a formula object is supplied it must have a Survr object as the response on the left of the ~ operator and a term on the right. For a single survival curve the "~1" part of the formula is required.

data

a data frame in wich to interpret the variables named in the formula.

type

a character string specifying the type of survival curve. Possible value are "pena-strawderman-hollander", "wang-chang" or "MLEfrailty". The default is "MLEfrailty". Only the first words are required, e.g "pe","wa","ML"

...

additional arguments passed to the type of estimator.

Details

See the help details of psh.fit, wc.fit or mlefrailty depending on the type chosen

Value

a survfitr object. Methods defined for survfitr objects are provided for print,plot,lines and summary.

Note

The mantainer wishes to thank Professors Chiung-Yu Huang and Shu-Hui Chang for their help for providing us with the Fortran code which computes standard errors of Wang and Chang's estimator.

References

1. Pe<f1>a, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. J. Amer. Statist. Assoc 96, 1299-1315.
2. Wang, M.-C. and Chang, S.-H. (1999). Nonparametric Estimation of a Recurrent Survival Function. J. Amer. Statist. Assoc 94, 146-153.

See Also

print.survfitr,plot.survfitr, lines.survfitr, summary.survfitr, Survr,psh.fit,wc.fit, mlefrailty.fit

Examples

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  data(colon)
  # fit a pena-strawderman-hollander and plot it
  fit<-survfitr(Survr(hc,time,event)~as.factor(dukes),data=colon,type="pena")
  plot(fit,ylim=c(0,1),xlim=c(0,2000))
  # print the survival estimators
  fit
  summary(fit)

  # fit a MLE Frailty and plot it (in this case do not show s.e.)
  fit<-survfitr(Survr(hc,time,event)~as.factor(dukes),data=colon,type="MLE")
  plot(fit)
  # print the survival estimators
  fit
  summary(fit)