LDM3df {survivalREC}

Landmark estimator for three gap times distribution function.

Description

Provides estimates for three gap times distribution function based on landmarking. The extension of the landmark estimator (LDM) to three gap times is a consequence of Bayes' theorem.

Usage

LDM3df(object, x, y, z)

Arguments

object

An object of class multidf.

x

The first time for obtaining estimates for the trivariate distribution function.

y

The second time for obtaining estimates for the trivariate distribution function.

z

The third time for obtaining estimates for the trivariate distribution function.

Value

Vector with the Landmark estimates for three gap times 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

LDM3df, LIN3df and WCH3df.

Examples

data("bladder5state")
b4state<-multidf(gap1=bladder5state$y1, event1=bladder4state$d1, 
                 gap2=bladder5state$y2, event2=bladder4state$d2,
                 gap3=bladder5state$y3, status=bladder4state$d3)
                 
head(b4state)[[1]]

LDM3df(b4state, x=13, y=20, z=40)

b4<-multidf(gap1=bladder4$t1, event1=bladder4$d1, 
            gap2=bladder4$t2-bladder4$t1, event2=bladder4$d2,
            gap3=bladder4$t3-bladder4$t2, status=bladder4state$d3)

LDM3df(b4,x=13,y=20,z=40)