bivrecNP {BivRec}

Nonparametric Analysis of Bivariate Alternating Recurrent Event Gap Time Data

Description

This function allows users to estimate the joint cumulative distribution function (cdf) for the two types of gap times (xij, yij), the marginal survival function for the Type I gap times (xij), and the conditional cdf for the Type II gap times (yij) given the Type I gap times (xij). See details for the estimation methods provided.

Usage

bivrecNP(response, ai, u1, u2, level, conditional, given.interval)

Arguments

response

A response object of class ⁠bivrecSurv⁠.

ai

See details.

u1

A vector or single number to be used for the estimation of joint cdf P(Type I gap times \le u1, Type II gap times \le u2) in the nonparametric method.

u2

A vector or single number to be used for the estimation of joint cdf P(Type I gap times \le u1, Type II gap times \le u2) in the nonparametric method.

level

The confidence level for confidence intervals for joint cdf, marginal survival probability and conditional cdf. Must be between 0.50 and 0.99. Default is 0.95.

conditional

A logical value. If TRUE, this function will calculate the conditional cdf for the Type II gap time given an interval of the Type I gap time and the bootstrap standard error and confidence interval at the specified confidence level. Default is FALSE.

given.interval

A vector c(v1, v2) that must be specified if conditional = TRUE. The vector indicates an interval for the Type I gap time to use for the estimation of the cdf of the Type II gap time given this interval. If given.interval = c(v1, v2), the function calculates P(Type II gap times \le y | v1 \le Type I gap times \le v2). The given values v1 and v2 must be in the range of gap times in the estimated marginal survival.

Details

⁠ai⁠ indicates a real non-negative function of censoring times to be used as weights in the nonparametric method. This variable can take on values of 1 or 2 which indicate:

• ⁠ai=1⁠ (default): the weights are simply 1 for all subjects, a(Ci) = 1.

• ⁠ai=2⁠: the weight for each subject is the subject's censoring time, a(Ci) = Ci.

Related methods: ⁠plot.bivrecNP⁠, ⁠head.bivrecNP⁠, ⁠print.bivrecNP⁠.

Value

A bivrecNP object that contains:

• ⁠joint_cdf⁠

• ⁠marginal_survival⁠

• ⁠conditional_cdf⁠ (when conditional = TRUE)

• ⁠formula⁠

• ⁠ai⁠

• ⁠level⁠

• ⁠given.interval⁠ (when conditional = TRUE)

• ⁠xij, yij⁠

• ⁠new_data⁠

References

Huang CY, Wang MC. (2005). Nonparametric estimation of the bivariate recurrence time distribution. Biometrics, 61: 392-402. doi: 10.1111/j.1541-0420.2005.00328.x

Examples

## Not run: 

library(BivRec)

# Simulate bivariate alternating recurrent event data
set.seed(28)
sim_data <- simBivRec(nsize=100, beta1=c(0.5,0.5), beta2=c(0,-0.5),
            tau_c=63, set=1.1)
bivrecsurv_data <- with(sim_data, bivrecSurv(id, epi, xij, yij, d1, d2))
npresult <- bivrecNP(response = bivrecsurv_data, ai=1,
                     u1 = seq(2, 20, 2), u2 = seq(1, 14, 2), level=0.99)
head(npresult)
plot(npresult)

#This is an example with longer runtime

 npresult2 <- bivrecNP(response = bivrecsurv_data, ai=1,
               u1 = seq(2, 20, 1), u2 = seq(1, 15, 1), conditional = TRUE,
               given.interval = c(0, 10), level = 0.99)
 head(npresult2)
 plot(npresult2)

## End(Not run)