# crossTime-methods: Computes the crossing survival times In YPBP: Yang and Prentice Model with Baseline Distribution Modeled by Bernstein Polynomials

## Description

Computes the crossing survival times along with their corresponding confidence/credible intervals.

## Usage

 ```1 2``` ```## S3 method for class 'ypbp' crossTime(object, newdata1, newdata2, conf.level = 0.95, nboot = 4000, ...) ```

## Arguments

 `object` an object of class ypbp `newdata1` a data frame containing the first set of explanatory variables `newdata2` a data frame containing the second set of explanatory variables `conf.level` level of the confidence/credible intervals; default is conf.level = 0.95 `nboot` number of bootstrap samples (default nboot=4000); ignored if approach="bayes". `...` further arguments passed to or from other methods.

## Value

the crossing survival time

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```# ML approach: library(YPBP) mle <- ypbp(Surv(time, status)~arm, data=ipass, approach="mle") summary(mle) newdata1 <- data.frame(arm=0) newdata2 <- data.frame(arm=1) tcross <- crossTime(mle, newdata1, newdata2, nboot = 100) tcross ekm <- survival::survfit(Surv(time, status)~arm, data=ipass) newdata <- data.frame(arm=0:1) St <- survfit(mle, newdata) plot(ekm, col=1:2) with(St, lines(time, surv[[1]])) with(St, lines(time, surv[[2]], col=2)) abline(v=tcross, col="blue") # Bayesian approach: bayes<-ypbp(Surv(time,status)~arm,data=ipass,approach="bayes",chains=2,iter=100) summary(bayes) newdata1 <- data.frame(arm=0) newdata2 <- data.frame(arm=1) tcross <- crossTime(bayes, newdata1, newdata2) tcross ekm <- survival::survfit(Surv(time, status)~arm, data=ipass) newdata <- data.frame(arm=0:1) St <- survfit(bayes, newdata) plot(ekm, col=1:2) with(St, lines(time, surv[[1]])) with(St, lines(time, surv[[2]], col=2)) abline(v=tcross, col="blue") ```

YPBP documentation built on July 1, 2020, 10:19 p.m.