Cumulative/dynamic ROC curve estimation

Martínez–Camblor, Pablo; Bayón, Gustavo F.; Pérez-Fernández, Sonia

doi:10.1080/00949655.2016.1175442

Cited by 19 publications

(34 citation statements)

References 27 publications

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“…The NPS method was proposed for estimating the ROC curve and AUC after reformulating the method of Heagerty et al. () by introducing a weight variable representing the event status of the subject during follow‐up (Li et al., ; Martínez‐Camblor et al., ). The idea behind this method is similar to the idea of imputation in the context of missing data.…”

Section: Methodsmentioning

confidence: 99%

“…(), and Martínez‐Camblor et al. (), we generate

(X, \log false(T^{u} false), \log false(C false))

from a trivariate normal distribution (MVN). More precisely,

[\begin{matrix} X_{i} \\ \log false(T_{i}^{u} false) \\ \log false(C_{i} false) \end{matrix}] \sim MVN (([], \begin{matrix} 0 \\ 0 \\ μ_{c} \end{matrix}), ([], \begin{matrix} 1 & ρ & 0 \\ ρ & 1 & 0 \\ 0 & 0 & 1 \end{matrix})),

where

ρ = - 0.9

, so that a higher value of the covariate X is an indication of a shorter duration T .…”

Section: Simulationsmentioning

confidence: 99%

“…The advantage of the latter over IPCW is that it does not assume independence between the prognostic tool and the censoring time. Martínez‐Camblor, Bayón, and Pérez‐Fernández () and Li, Greene, and Hu () independently proposed another method for ROC curve estimation. In their method, the missing status indicator for the censored subjects is replaced by weights obtained from the conditional survival function.…”

Section: Introductionmentioning

confidence: 99%

“…Martínez‐Camblor et al. () used a semiparametric Cox model and Kaplan–Meier estimator for conditional survival function estimation, whereas Li et al. () used a weighted Kaplan–Meier estimator.…”

Section: Introductionmentioning

confidence: 99%

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On the validity of time‐dependent AUC estimation in the presence of cure fraction

2019

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During the last decades, several approaches have been proposed to estimate the timedependent area under the receiver operating characteristic curve (AUC) of risk tools derived from survival data. The validity of these estimators relies on some regularity assumptions among which a survival function being proper. In practice, this assumption is not always satisfied because a fraction of the population may not be susceptible to experience the event of interest even for long follow-up. Studying the sensitivity of the proposed estimators to the violation of this assumption is of substantial interest. In this paper, we investigate the performance of a nonparametric simple estimator, developed for classical survival data, in the case when the population exhibits a cure fraction. Motivated from the current practice of deriving risk tools in oncology and cardiovascular disease prevention, we also assess the loss, in terms of predictive performance, when deriving risk tools from survival models that do not acknowledge the presence of cure. The simulation results show that the investigated method is valid even under the presence of cure. They also show that risk tools derived from survival models that ignore the presence of cure have smaller AUC compared to those derived from survival models that acknowledge the presence of cure. This was also attested with a real data analysis from a breast cancer study. K E Y W O R D SCox model, mixture cure models, promotion time models, risk tools, time-dependent AUC 1430

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Section: Methodsmentioning

confidence: 99%

“…(), and Martínez‐Camblor et al. (), we generate

(X, \log false(T^{u} false), \log false(C false))

from a trivariate normal distribution (MVN). More precisely,

[\begin{matrix} X_{i} \\ \log false(T_{i}^{u} false) \\ \log false(C_{i} false) \end{matrix}] \sim MVN (([], \begin{matrix} 0 \\ 0 \\ μ_{c} \end{matrix}), ([], \begin{matrix} 1 & ρ & 0 \\ ρ & 1 & 0 \\ 0 & 0 & 1 \end{matrix})),

where

ρ = - 0.9

, so that a higher value of the covariate X is an indication of a shorter duration T .…”

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the validity of time‐dependent AUC estimation in the presence of cure fraction

2019

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“…based on the Nelson-Aalen cumulative incidence curve (Wolf et al [12]). Martínez-Camblor et al [13] and Li et al [14] studied estimators where the missing status indicator is replaced by weights obtained from conditional survival functions.…”

mentioning

confidence: 99%

Smooth time-dependent receiver operating characteristic curve estimators

Martínez–Camblor

Pardo–Fernández

2017

Stat Methods Med Res

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The receiver operating characteristic curve is a popular graphical method often used to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, two main extensions have been proposed: the cumulative/dynamic receiver operating characteristic curve and the incident/dynamic receiver operating characteristic curve. In both cases, the main problem for developing appropriate estimators is the estimation of the joint distribution of the variables time-to-event and marker. As usual, different approximations lead to different estimators. In this article, the authors explore the use of a bivariate kernel density estimator which accounts for censored observations in the sample and produces smooth estimators of the time-dependent receiver operating characteristic curves. The performance of the resulting cumulative/dynamic and incident/dynamic receiver operating characteristic curves is studied by means of Monte Carlo simulations. Additionally, the influence of the choice of the required smoothing parameters is explored. Finally, two real-applications are considered. An R package is also provided as a complement to this article.

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Time‐dependent ROC curve estimation for interval‐censored data

Beyene

Ghouch²

2022

Biometrical J

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The receiver‐operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker. In time‐to‐event studies, the subject's event status is time‐dependent, and hence, time‐dependent extensions of ROC curve have been proposed. However, in practice, the calculation of this curve is not straightforward due to the presence of censoring that may be of different types. Existing methods focus on the more standard and simple case of right‐censoring and neglect the general case of mixed interval‐censored data that may involve left‐, right‐, and interval‐censored observations. In this context, we propose and study a new time‐dependent ROC curve estimator. We also consider some summary measures (area under the ROC curve and Youden index) traditionally associated with ROC as well as the Youden‐based cutoff estimation method. The proposed method uses available data very efficiently. To this end, the unknown status (positive or negative) of censored subjects are estimated from the data via the estimation of the conditional survival function given the marker. For that, we investigate both model‐based and nonparametric approaches. We also provide variance estimates and confidence intervals using Bootstrap. A simulation study is conducted to investigate the finite sample behavior of the proposed methods and to compare their performance with a competitor. Globally, we observed better finite sample performances for the proposed estimators. Finally, we illustrate the methods using two data sets one from a hypobaric decompression sickness study and the other from an oral health study. The proposed methods are implemented in the R package cenROC.

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Cumulative/dynamic ROC curve estimation

Cited by 19 publications

References 27 publications

On the validity of time‐dependent AUC estimation in the presence of cure fraction

On the validity of time‐dependent AUC estimation in the presence of cure fraction

Smooth time-dependent receiver operating characteristic curve estimators

Time‐dependent ROC curve estimation for interval‐censored data

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