LDMdf: Landmark estimator for the bivariate distribution function

In gsoutinho/survrec: Estimation of the multivariate distributions for censored gap times

Description

Provides estimates for the bivariate distribution function based on Bayes' theorem and Kaplan-Meier survival function. This approach is also named as landmarking.

Usage

LDMdf(object, x, y)

Arguments

object	An object of class multidf.
x	The first time for obtaining estimates for the bivariate distribution function.
y	The second time for obtaining estimates for the bivariate distribution function.

Author(s)

Gustavo Soutinho and Luis Meira-Machado

References

van Houwelingen, H.C. (2007). Dynamic prediction by landmarking in event history analysis, Scandinavian Journal of Statistics, 34, 70-85.

Kaplan, E. and Meier, P. (1958). Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association 53(282), 457-481.

See Also

IPCWdf, KMWdf, LINdf and WCHdf.

Examples

data("bladder4state")
b3state <- multidf(time1=bladder4state$y1, event1=bladder4state$d1,
                  time=bladder4state$y1+bladder4state$y2,status=bladder4state$d2)
               
LDMdf(b3state,x=13,y=20)

gsoutinho/survrec documentation built on Dec. 20, 2021, 1:46 p.m.