Estimation of the bivariate distribution function for censored gap times

Moreira, António H. J.; Araújo, Artur; Meira‐Machado, Luís

doi:10.1080/03610918.2014.963609

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…To handle right censoring, inverse probability of censoring weighting (IPCW) can be used (see, for example, [24] for further details).…”

Section: Inverse Probability Of Censoring Weighted Estimatorsmentioning

confidence: 99%

“…It has been shown that in this setting, the correlation of the FGM varies between 0 (independent gap times) for δ = 0 and 0.25 for δ = 1. The FGM was also used in the papers by [15] and [24].…”

Section: Scenario 1: Two Gap Timesmentioning

confidence: 99%

“…This line of research has received much attention recently. Among others, they were investigated by [6,8,15,24,27,35,36,40] whose interest was focused on the estimation of the bivariate distribution of the gap times. In other cases, the interest was more focused on the distribution of the gap times, such as the estimation of the joint gap times, the gap time survival functions, or the conditional survival function of the gap times [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric estimation of the distribution of gap times for recurrent events

Soutinho

Meira‐Machado

2022

Stat Methods Appl

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In many longitudinal studies, information is collected on the times of different kinds of events. Some of these studies involve repeated events, where a subject or sample unit may experience a well-defined event several times throughout their history. Such events are called recurrent events. In this paper, we introduce nonparametric methods for estimating the marginal and joint distribution functions for recurrent event data. New estimators are introduced and their extensions to several gap times are also given. Nonparametric inference conditional on current or past covariate measures is also considered. We study by simulation the behavior of the proposed estimators in finite samples, considering two or three gap times. Our proposed methods are applied to the study of (multiple) recurrence times in patients with bladder tumors. Software in the form of an R package, called survivalREC, has been developed, implementing all methods.

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“…To handle right censoring, inverse probability of censoring weighting (IPCW) can be used (see, for example, [24] for further details).…”

Section: Inverse Probability Of Censoring Weighted Estimatorsmentioning

confidence: 99%

Section: Scenario 1: Two Gap Timesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonparametric estimation of the distribution of gap times for recurrent events

Soutinho

Meira‐Machado

2022

Stat Methods Appl

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survivalBIV: Estimation of the Bivariate Distribution Function for Sequentially Ordered Events Under Univariate Censoring

Moreira¹,

Machado²

2012

J. Stat. Soft.

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In many medical studies, patients can experience several events. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider the estimation of the bivariate distribution function for censored gap times, using survivalBIV a software application for R. Some related problems such as the estimation of the marginal distribution of the second gap time is also discussed. It describes the capabilities of the program for estimating these quantities using four different approaches, all using the Kaplan-Meier estimator of survival. One of these estimators is based on Bayes' theorem and Kaplan-Meier survival function. Two estimators were recently proposed using the Kaplan-Meier estimator pertaining to the distribution of the total time to weight the bivariate data (de Uña-Álvarez and Meira-Machado 2008 and de Uña-Álvarez and Amorim 2011). The software can also be used to implement the estimator proposed in Lin, Sun, and Ying (1999), which is based on inverse probability of censoring weighted. The software is illustrated using data from a bladder cancer study.

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Estimation of the bivariate distribution function for censored gap times

Cited by 2 publications

References 19 publications

Nonparametric estimation of the distribution of gap times for recurrent events

Nonparametric estimation of the distribution of gap times for recurrent events

survivalBIV: Estimation of the Bivariate Distribution Function for Sequentially Ordered Events Under Univariate Censoring

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