IntROC: Time-dependent ROC curve estimation for interval-censored...

In cenROC: Estimating Time-Dependent ROC Curve and AUC for Censored Data

Time-dependent ROC curve estimation for interval-censored survival data

Description

This function computes the time-dependent ROC curve for interval censored survival data using the cumulative sensitivity and dynamic specificity definitions. The ROC curves can be either empirical (non-smoothed) or smoothed with/without boundary correction. It also calculates the time-dependent AUC.

Usage

IntROC(L, R, M, t, U = NULL, method = "emp", method2 = "pa", dist = "weibull",
        bw = NULL, ktype = "normal", len = 151, B = 0, alpha = 0.05, plot = "TRUE")

Arguments

L	The numericvector of left limit of observed time. For left censored observations L == 0.
R	The numericvector of right limit of observed time. For right censored observation R == inf.
M	The numeric vector of marker values.
t	A scaler time point used to calculate the ROC curve.
U	The numeric vector of cutoff values.
method	The method of ROC curve estimation. The possible options are "emp" empirical metod; "untra" smooth without boundary correction and "tra" is smooth ROC curve estimation with boundary correction. The default is the "emp" empirical method.
method2	A character indication type of modeling. This include nonparametric "np", parmetric "pa" and semiparametric "sp". The default is the "np" parametric with weibull distribution.
dist	A character incating the type of distribution for parametric model. This includes are "exponential", "weibull", "gamma", "lnorm", "loglogistic" and "generalgamma".
bw	A character string specifying the bandwidth estimation method. The possible options are "NR" for the normal reference, the plug-in "PI" and the cross-validation "CV". The default is the "NR" normal reference method. It is also possible to use a numeric value.
ktype	A character string giving the type kernel distribution to be used for smoothing the ROC curve: "normal", "epanechnikov", "biweight", or "triweight". By default, the "normal" kernel is used.
len	The length of the grid points for ROC estimation. Default is 151.
B	The number of bootstrap samples to be used for variance estimation. The default is 0, no variance estimation.
alpha	The significance level. The default is 0.05.
plot	The logigal parameter to see the ROC curve plot. Default is TRUE.

Details

This function implments time-dependent ROC curve and the corresponding AUC using the model-band and nonparametric for the estimation of conditional survival function. The empirical (non-smoothed) ROC estimate and the smoothed ROC estimate with/without boundary correction can be obtained using this function. The smoothed ROC curve estimators require selecting a bandwidth parametr for smoothing the ROC curve. To this end, three data-driven methods: the normal reference "NR", the plug-in "PI" and the cross-validation "CV" were implemented. See Beyene and El Ghouch (2020) for details.

Value

Returns the following items:

ROC The vector of estimated ROC values. These will be numeric numbers between zero

and one.

U The vector of grid points used.

AUC A data frame of dimension 1 \times 4. The columns are: AUC, standard error of AUC, the lower

and upper limits of bootstrap CI.

bw The computed value of bandwidth. For the empirical method this is always NA.

Dt The vector of estimated event status.

M The vector of Marker values.

References

Beyene, K. M. and El Ghouch A. (2022). Time-dependent ROC curve estimation for interval-censored data. Biometrical Journal, 64, 1056– 1074.

Beyene, K. M. and El Ghouch A. (2020). Smoothed time-dependent receiver operating characteristic curve for right censored survival data. Statistics in Medicine. 39: 3373– 3396.

Examples

library(cenROC)

data(hds)

est = IntROC(L=hds$L, R=hds$R, M=hds$M, t=2)
est$AUC

cenROC documentation built on March 31, 2023, 5:19 p.m.