bivrecNP {BivRec} R Documentation

## 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 ≤ u1, Type II gap times ≤ 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 ≤ u1, Type II gap times ≤ 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 ≤ y | v1 ≤ Type I gap times ≤ 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)

```

[Package BivRec version 1.2.1 Index]