Title:	Parametric Time-Dependent Receiver Operating Characteristic
Version:	0.2.0
Maintainer:	Faiz Azhar <faiz.azhar241@gmail.com>
Description:	Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario.
License:	MIT + file LICENSE
URL:	https://github.com/FaizAzhar/parTimeROC
BugReports:	https://github.com/FaizAzhar/parTimeROC/issues
Encoding:	UTF-8
RoxygenNote:	7.3.1
Biarch:	true
Depends:	R (≥ 3.4.0)
Imports:	cubature, DescTools, flexsurv (≥ 2.2.2), GofCens, graphics, Matrix, methods, moments, mvtnorm, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.4.0), sn (≥ 2.1.1), stats, survival (≥ 3.5.5), VineCopula (≥ 2.4.5)
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
SystemRequirements:	GNU make
Suggests:	spelling, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Language:	en-US
NeedsCompilation:	yes
Packaged:	2025-07-11 18:36:11 UTC; farea
Author:	Faiz Azhar [aut, cre, cph], Adina Najwa Kamarudin [cph, ths], Universiti Teknologi Malaysia, UTM [cph], Universiti Malaya, UM [cph]
Repository:	CRAN
Date/Publication:	2025-07-11 19:00:02 UTC

The 'parTimeROC' package.

Description

The goal of parTimeROC is to store methods and procedures needed to run the time-dependent ROC analysis parametrically. This package adopts two different theoretical framework to produce the ROC curve which are from the proportional hazard model and copula function. Currently, this package only able to run analysis for single covariate/biomarker with survival time. The future direction for this work is to be able to include analysis for multiple biomarkers with longitudinal measurements.

_PACKAGE

References

Stan Development Team (NA). RStan: the R interface to Stan. R package version 2.32.3. https://mc-stan.org

get.copula

Description

Storing list of copula.

Usage

get.copula

Format

An object of class list of length 5.

Value

A list of copula. #' @examples get.copula #> "gaussian" "clayton90" "gumbel90" "gumbel" "joe90"

get.distributions

Description

Storing list of distributions for Biomarker, X and Time-to-event, T.

Usage

get.distributions

Format

An object of class list of length 9.

Value

A list of distributions.

Examples

get.distributions
#> "exponential" "weibull"     "gaussian"    "normal"      "lognormal"
#> "gompertz"    "skewnormal"

print.TimeROC

Description

print.TimeROC

Usage

## S3 method for class 'TimeROC'
print(x, ...)

Arguments

Value

Summarize model's info in console

Examples

test <- timeroc_obj(dist = 'lognormal-weibull-PH')
print(test)

print.fitTROC

Description

print.fitTROC

Usage

## S3 method for class 'fitTROC'
print(x, ...)

Arguments

Value

Summarize estimated parameters in console

Examples

test <- timeroc_obj(dist = 'lognormal-weibull-PH')
rr <- rtimeroc(test, n=300, params.x=c(meanlog=1,sdlog=0.8),
               params.t = c(shape=1.6,scale=1.2),
               params.ph = 1.1)
cc <- timeroc_fit(test, rr$x, rr$t, rr$event)
print(cc)

rate_change

Description

Function to compute the rate change of the ROC with respect to dx and dt.

Usage

rate_change(
  obj,
  t,
  n = 3,
  type = "standard",
  params.x,
  params.t,
  copula,
  definition = "c/d",
  params.copula,
  params.ph,
  seed,
  cutoff = 100,
  newx
)

Arguments

Value

A list of rate change dt, dx and the angle between these rate of change.

Examples

## Not run: 
## Using 'fitROC' object
test <- timeroc_obj("normal-weibull-copula", copula = "gaussian")
rr <- rtimeroc(test, n=500,
               params.x = c(mean=5, sd=0.8),
               params.t = c(shape=1.6, scale=5),
              params.copula = -0.3)
cc <- timeroc_fit(test, x = rr$x, t = rr$t, event = rr$event)
jj <- rate_change(cc, t = c(1,2,10,11))

## Using 'TimeROC' object
test <- timeroc_obj("normal-weibull-PH",
params.x = c(mean=5, sd=0.8),
params.t = c(shape=1.6, scale=5),
params.ph = 1)
ee <- rate_change(test, t = c(.1,.2))

## End(Not run)

rtimeroc

Description

Function to generate bivariate data from PH or copula model.

Usage

rtimeroc(obj, n, censor.rate = 0, params.x, params.t, params.copula, params.ph)

Arguments

Value

A dataframe with 3 columns (x = biomarker value, t = observable time-to-event, status = censored/not censor (0 or 1))

Examples

## Copula model
test <- timeroc_obj(dist = 'gompertz-gompertz-copula', copula = "gumbel90")
set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0, n=500,
               params.t = c(shape=3,rate=1),
               params.x = c(shape=1,rate=2),
               params.copula=-5)
plot(t~x, rr)

## PH model
test <- timeroc_obj(dist = 'weibull-gompertz-PH')
set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0, n=100,
               params.t = c(shape=2, rate=1),
               params.x = c(shape=2, scale=1),
               params.ph=0.5)
plot(t~x, rr)

timeroc_auc

Description

Function to compute the area under the ROC curve

Usage

timeroc_auc(obj)

Arguments

Value

A dataframe of the area under the ROC curve

Examples

## Not run: 
test <- timeroc_obj('normal-weibull-copula', copula = 'clayton90')
print(test)

set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0.1, n=500,
               params.t = c(shape=1, scale=5),
               params.x = c(mean=5, sd=1),
               params.copula=-2)

cc <- timeroc_fit(x=rr$x, t=rr$t, event=rr$event, obj = test)

jj <- timeroc_predict(cc, t = quantile(rr$t, probs = c(0.25,0.5,0.75)))

timeroc_auc(jj)

## End(Not run)

timeroc_fit

Description

Fit TimeROC using Maximum Likelihood Estimator.

Usage

timeroc_fit(
  obj,
  x,
  t,
  event,
  init.param.x = NULL,
  init.param.t = NULL,
  init.param.copula = NULL,
  init.param.ph = NULL,
  ci = 0.95,
  method = "mle",
  weights = NULL,
  breakpoints = NULL,
  init_bayes = NULL
)

Arguments

Value

return a list of frequentist or bayesian estimator.

Examples

## Not run: 
## fitting copula model
test <- timeroc_obj(dist = 'gompertz-gompertz-copula', copula = "gumbel90")
set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0, n=500,
               params.t = c(shape=3,rate=1),
               params.x = c(shape=1,rate=2),
               params.copula=-5) # name of parameter must follow standard

plot(t~x, rr)
start.t <- Sys.time()
cc <- timeroc_fit(rr$x, rr$t, rr$event, obj = test)
print(Sys.time()-start.t)

## fitting PH model
test <- timeroc_obj(dist = 'weibull-lognormal-PH')
set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0, n=100,
               params.t = c(meanlog=0, sdlog=1),
               params.x = c(shape=2, scale=1),
               params.ph=0.5) # name of parameter must follow standard
plot(t~x, rr)
start.t <- Sys.time()
cc <- timeroc_fit(rr$x, rr$t, rr$event, obj = test)
print(Sys.time()-start.t)

## End(Not run)

timeroc_gof

Description

Function to compute goodness-of-fit for the Proportional Hazard (PH) or copula model. For PH model, the Cox-Snell residuals are computed and compared with Exponential(rate=1). For copula model, the Rosenblatt transformation is applied before performing independence testing. Kolmogorov-Smirnov test is performed to check the goodness-of-fit of the biomarker and time-to-event.

Usage

timeroc_gof(obj)

Arguments

Value

A list of test statistics and p-values. Automatically plot residuals for biomarker and time-to-event.

Examples

## Not run: 
# Copula model
rt <- timeroc_obj("normal-weibull-copula",copula="clayton90")
set.seed(1)
rr <- rtimeroc(rt, n=50, censor.rate = 0,
               params.x = c(mean=5, sd=1),
               params.t = c(shape=1, scale=5),
               params.copula = -2.5)
plot(t~x, data=rr)
test <- timeroc_obj("normal-weibull-copula",copula="gumbel90")
jj <- timeroc_fit(test, rr$x, rr$t, rr$event)

cc <- timeroc_gof(jj)

test <- timeroc_obj("normal-weibull-copula",copula="clayton90")
jj <- timeroc_fit(test, rr$x, rr$t, rr$event)

cc <- timeroc_gof(jj)

## End(Not run)

timeroc_obj

Description

Function to initialized time-dependent ROC object.

Usage

timeroc_obj(
  dist,
  params.x = NA,
  params.t = NA,
  copula = NA,
  params.copula = NA,
  params.ph = NA
)

Arguments

Value

A 'TimeROC' object.

Examples

## Copula model
test <- timeroc_obj(dist = 'gompertz-gompertz-copula', copula = "gumbel90")

## PH model
test <- timeroc_obj(dist = 'weibull-gompertz-PH')

timeroc_predict

Description

Predict time-dependent ROC from fitted model.

Usage

timeroc_predict(
  obj,
  t,
  newx,
  cutoff = 100,
  B = 1,
  type = "standard",
  params.x,
  params.t,
  copula,
  method = "mle",
  definition = "c/d",
  seed,
  params.copula,
  params.ph,
  ci = 0.95,
  h = -1e-04
)

Arguments

Value

A list of ROC dataframe for each time-to-event.

Examples

## Not run: 
# PH model
test <- timeroc_obj('normal-weibull-PH')
set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0.1, n=500,
               params.t = c(shape=1, scale=5),
               params.x = c(mean=5, sd=1),
               params.ph=0.5)
cc <- timeroc_fit(x=rr$x, t=rr$t, event=rr$event, obj = test)
start.t <- Sys.time()
jj <- timeroc_predict(cc)
print(Sys.time()-start.t)


# Copula model
test <- timeroc_obj(dist = 'gompertz-gompertz-copula', copula='clayton90',
params.t = c(shape=3,rate=1),
params.x = c(shape=1,rate=2),
params.copula=-5)
set.seed(23456)
rr <- rtimeroc(obj = test, censor.rate = 0.2, n=500)
cc <- timeroc_fit(x=rr$x, t=rr$t, event=rr$event, obj = test)
start.t <- Sys.time()
jj <- timeroc_predict(cc)
print(Sys.time()-start.t)

## End(Not run)