Title: Doubly Truncated Data Analysis, Cumulative Incidence Functions
Version: 1.0.2
Maintainer: José Carlos Soage González <jsoage@uvigo.es>
Description: Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.
Depends: R (≥ 3.5.0)
License: GPL-2
Encoding: UTF-8
Imports: doParallel, foreach, Rcpp
LinkingTo: Rcpp
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2020-02-13 12:06:20 UTC; Matlab
Author: Jacobo de Uña Álvarez [aut], José Carlos Soage González [cre]
Repository: CRAN
Date/Publication: 2020-02-13 12:30:02 UTC

Doubly Truncated Data Analysis, Cumulative Incidence Functions

Description

Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting.

Details

Value

Acknowledgements

Author(s)

References


Doubly Truncated Data Analysis, Cumulative Incidence Functions

Description

This function computes a nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting.

Usage

DTDAcif(x, u, v, comp.event, method = c("indep", "dep"), boot = F,
  B = 300, N.iter = 100, error = 1e-06)

Arguments

x

Numeric vector corresponding to the variable of ultimate interest.

u

Numeric vector corresponding to the left truncation variable.

v

Numeric vector corresponding to the right truncation variable.

comp.event

Competing risk indicator.

method

The method used to compute the nonparametric estimator. Use ‘indep’ for independent truncation variables and “dep“ for truncation variables possibly depending on the competing risk.

boot

Logical. If TRUE the bootstrap standard deviation of the cumulative incidences is calculated.

B

Number of bootstrap replicates.

N.iter

Maximum number of iterations.

error

Error criterion for convergence.

Details

The nonparametric estimator is based on the Efron-Petrosian NPMLE (Efron and Petrosian, 1999). Actually, each pair (Xi,Zi) -where Xi stands for the variable of interest and Zi is the competing event indicator- is weighted by the jump of the Efron-Petrosian NPMLE at Xi (method=“indep"), or by a normalized version of the Efron-Petrosian NPMLE computed from the subset of (Xs,Zs)'s such that Zs=Zi (method=“dep”). The former is suitable when the truncating couple (U,V) is independent of (X,Z), while the latter is recommended when (U,V) and X are only conditionally independent given Z; see de Uña-Álvarez (2019) for a full description of the estimators and of their properties. When the competing event indicator is missing, the function simply computes the Efron-Petrosian NPMLE and the argument method has no role.

Value

A list containing:

Acknowledgements

Author(s)

References

Examples





set.seed(1234)
n <- 50  # sample size

x <- runif(n, 0, 1)  # time variable of interest
z <- rbinom(n, 1, 1 / 4)   # competing event indicator

# truncation variables

u <- runif(n, -.25, .5)  # left truncation variable
v <- u + .75   # right truncation variable

# note: (u,v) is independent of (x,z) so both estimation methods are consistent

# truncating the sample:

for (i in 1:n) {
  while (u[i] > x[i] | v[i] < x[i]) {
    x[i] <- runif(1, 0, 1)
    z[i] <- rbinom(1, 1, 1 / 4)
    u[i] <- runif(1, -.25, .5)
    v[i] <- u[i] + .75
  }
}

# note: (u,v) since is independent of (x,z)
# both estimation methods are consistent:

res.i <- DTDAcif(x, u, v, z, method = "indep", boot = TRUE)
res.d <- DTDAcif(x, u, v, z, method = "dep", boot = TRUE)

oldpar <- par(mfrow=c(1,2))
plot(res.i, main = "Indep trunc", intervals = TRUE)
plot(res.d, main = "Cond indep trunc", intervals = TRUE)

summary(res.i)
summary(res.d)

plot(res.i$data$x, res.i$biasf, type = "s")  # the observational bias
# the observational bias, event 1
plot(res.d$data$x[res.d$data$z == 1], res.d$biasf$biasf_1, type = "s")
# the observational bias, event 2
lines(res.d$data$x[res.d$data$z == 2], res.d$biasf$biasf_2, type = "s", col = 2)
par(oldpar)



plot.DTDAcif

Description

S3 method to plot a DTDAcif object by using the generic plot function.

Usage

## S3 method for class 'DTDAcif'
plot(x, intervals = FALSE, level = 0.95, main = "",
  xlab = "", ylab = "", ylim, xlim, ...)

Arguments

x

DTDAcif object.

intervals

Logical. If TRUE confidence intervals are calculated if standard deviation was calculated before.

level

Confidence level of the standard deviation of the cifs. Default is 0.95.

main

An overall title for the plot.

xlab

A title for the x axis.

ylab

A title for the y axis.

ylim

Limit over the y axis.

xlim

Limit over the x axis.

...

Additional parameters.

Author(s)


summary.DTDAcif

Description

S3 method to summarize a DTDAcif object by using the generic summary function.

Usage

## S3 method for class 'DTDAcif'
summary(object, ...)

Arguments

object

DTDAcif object.

...

Additonal parameters.

Author(s)