A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

Dai, Hongsheng; Restaino, Marialuisa; Wang, Huan

doi:10.1080/10485252.2016.1225734

Cited by 4 publications

(8 citation statements)

References 19 publications

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“…The cure and death event will be subject to right censoring. Therefore, a future research work will be to study the under-reporting probabilities using bivariate truncation and censoring methodologies (Dai and Fu 2012;Dai, Restaino, and Wang 2016;Dai, Wang, Restaino, and Bao 2018;Wang, Dai, and Fu 2013), under the competing risk model framework.…”

Section: Discussionmentioning

confidence: 99%

Truncation data analysis for the under-reporting probability in COVID-19 pandemic

Restaino

2021

Journal of Nonparametric Statistics

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The COVID-19 pandemic has affected all countries in the world and brings a major disruption in our daily lives. Estimation of the prevalence and contagiousness of COVID-19 infections may be challenging due to under-reporting of infected cases. For a better understanding of such pandemic in its early stages, it is crucial to take into consideration unreported infections. In this study we propose a truncation model to estimate the under-reporting probabilities for infected cases. Hypothesis testing on the differences in truncation probabilities, that are related to the under-reporting rates, is implemented. Large sample results of the hypothesis test are presented theoretically and by means of simulation studies. We also apply the methodology to COVID-19 data in certain countries, where under-reporting probabilities are expected to be high.

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Section: Discussionmentioning

confidence: 99%

Truncation data analysis for the under-reporting probability in COVID-19 pandemic

Restaino

2021

Journal of Nonparametric Statistics

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“…we will call quasi-empirical distribution (or qED), since it actually generalizes a similar expression of quasi-empirical distribution for censored data (16). Indeed, if in (10) we assume that T k ≡ ∅, k = 1, . .…”

Section: Quasi-empirical Distributionmentioning

confidence: 99%

“…3). These factors determine the structure of the truncated-censored sample (10), which is characterized by the type of truncating and censoring sets, their number, and relative position in the sample. The structure of such sample can be approximated by the following indicators: -the type of truncating and censoring sets available in the sample (possible variants of truncated-censored observations are given in Table 1); -the degree of data truncation and the degree of censoring (the proportion of truncated and censored observations in the sample), differentiated by types of truncation and censoring sets.…”

Section: Double-truncatedmentioning

confidence: 99%

“…The objective is to draw conclusions on unconditional distribution P of the initial vector X based on the data of type (10).…”

Section: Examples Of Truncated and Censored Datamentioning

confidence: 99%

“…For example, Frees et al [13], Carriere [7], Wang [35] and Luciano et al [24] consider copula models for describing the bivariate distribution function. Dai and Bao [9], Dai et al [10], proposed an algorithm allowing the use of various parametric functions to transform bivariate data into univariate data, and evaluating the univariate distribution function using the Kaplan-Meier estimator and its inverse transformation into bivariate distribution. Here, the estimate of the bivariate distribution function is ambiguous, depending on the type of function used to convert data.…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric estimation of multivariate distribution function for truncated and censored lifetime data

Baskakov

Bartunova²

2019

Eur. Actuar. J.

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1 A number of models for generating statistical data in various fields of insurance, including life insurance, pensions, and general insurance have been considered. It is shown that the insurance statistics data, as a rule, are truncated and censored, and often multivariate. We propose a non-parametric estimation of the distribution function for multivariate truncated-censored data in the form of a quasi-empirical distribution and a simple iterative algorithm for its construction. To check the accuracy of the proposed evaluation of the distribution function for truncated-censored data, simulation studies have been conducted, which showed its high efficiency. The proposed estimates have been tested for many years by the IAAC Group of Companies in the actuarial valuation of corporate social liabilities according to IAS 19 Employee Benefits. Apart from insurance, some results of the work can be used, for example in medicine, biology, demography, mathematical theory of reliability, etc.

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A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes

Dobler,

Möllenhoff

2024

Statistics in Medicine

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A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy‐to‐interpret parameter. Hence, we seek to develop a new estimand‐driven method to compare the effectiveness of two treatments in the context of right‐censored survival data with matched pairs. With the help of competing risks techniques, the so‐called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling‐based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well‐known benchmark data set from a trial with patients suffering from diabetic retinopathy.

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A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

Cited by 4 publications

References 19 publications

Truncation data analysis for the under-reporting probability in COVID-19 pandemic

Truncation data analysis for the under-reporting probability in COVID-19 pandemic

Nonparametric estimation of multivariate distribution function for truncated and censored lifetime data

A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes

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