Quantile residual lifetime with right-censored and length-biased data

Liu, Peng; Wang, Yixin; Zhou, Yong

doi:10.1007/s10463-014-0482-9

Cited by 6 publications

(8 citation statements)

References 33 publications

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“…The new estimator has the same spirit of the composite likelihood method but a simpler format than the estimator proposed in Huang & Qin (). Besides compared with Liu, Wang, & Zhou (), our proposed estimator has a nicely enclosed‐form expression of the estimated survival function. This helps to estimate the quantile residual lifetime more easily and directly by solving the proposed estimating equations.…”

Section: Introductionmentioning

confidence: 93%

“…Recently Liu, Wang, & Zhou () studied the quantile residual lifetime with length‐biased and right‐censored data based on the nonparametric maximum likelihood estimation approach (Asgharian, M'Lan, & Wolfson, ; Asgharian & Wolfson, ), but the estimated distribution of the biased survival time does not have a closed‐form expression, which complicates the computation and asymptotic properties derivation of the quantile residual lifetime estimator. Besides Wang, Liu, & Zhou () proposed an estimator of the quantile residual lifetime under left‐truncated and right‐censored data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data

Wang

Zhou

et al. 2017

Can J Statistics

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Quantile residual lifetime models are often of concern in survival analysis, especially when studying a chronic or irreversible disease like dementia. In the past several decades residual life models have been studied extensively with right‐censored survival data. However these methods are not suitable to analyze the length‐biased and right‐censored data from the prevalent cohort sampling. In this article we propose nonparametric and semiparametric model‐based procedures to estimate the quantile residual lifetime with censored length‐biased data. Two test statistics are established for comparing the quantile residual lifetimes of two groups, evaluated, respectively, on ratio and difference in terms of type I error probabilities and powers. Some simulations are conducted to compare the proposed method with existing approaches. Real dementia data from the National Alzheimer's Coordinating Center are used to illustrate the proposed estimation methods by estimating the quantile residual lifetimes of the dementia patients. The Canadian Journal of Statistics 45: 220–250; 2017 © 2017 Statistical Society of Canada

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data

Wang

Zhou

et al. 2017

Can J Statistics

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“…8 We note that when the observed failure times are length-biased, the censoring mechanism is informative yielding an NPMLE which places mass on both the observed failure/censoring times of the prevalent cohort study. [8][9][10][11] Thus, the unbiased survival function estimator is fully defined over the entire real line establishing the existence of the median point estimate. While other product-limit estimators for left-truncated right-censored failure time data may be applied (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

A nonparametric test for equality of survival medians using right-censored prevalent cohort survival data

McVittie

Asgharian

2022

Stat Methods Med Res

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The median is a robust summary commonly used for comparison between populations. The existing literature falls short in testing for equality of survival medians when the collected data do not form representative samples from their respective target populations and are subject to right censoring. Such data commonly occur in prevalent cohort studies with follow-up. We consider a particular case where the disease under study is stable, that is, the incidence rate of the disease is stable. It is known that survival data collected on diseased cases, when the disease under study is stable, form a length-biased sample from the target population. We fill the gap for the particular case of length-biased right-censored survival data by proposing a large-sample test using the nonparametric maximum likelihood estimator of the survivor function in the target population. The small sample performance of the proposed test statistic is studied via simulation. We apply the proposed method to test for differences in survival medians of Alzheimer’s disease and dementia groups using the survival data collected as part of the Canadian Study of Health and Aging.

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“…Shi et al (2018) showed how to estimate the quantiles from length-biased and right-censored data based on the NPMLE. Liu et al (2015) provided NPMLE of the quantile residual lifetime with length-biased and rightcensored data. Since it is based on survival distribution estimate which does not have a closed-form expression, computation of this estimator and its asymptotic properties is relatively complicated.…”

Section: Introductionmentioning

confidence: 99%

“…Since it is based on survival distribution estimate which does not have a closed-form expression, computation of this estimator and its asymptotic properties is relatively complicated. Although Liu et al (2015) approach can be extended to censored length-biased data, but this method does not utilize the information of the truncation variable, making it less efficient than that proposed in Wang et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Inference on quantile residual life for length-biased survival data

Lopa

2020

JSR

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Length biased data occurs when a prevalent sampling is used to recruit subject into a study that investigates the time from an initial event to a terminal event. Such data are usually left-truncated and right-censored. While there have been accurate and efficient methods to estimate the survival function, not much work have been done regarding estimation of residual life time distribution or the summary parameters such as the median and quantiles of the residual life. In this paper we propose two ways to estimate the quantiles of the residual life time at fixed time points accounting for the length biased and censored na- ture of the data. We provide the asymptotic properties of these estimators and investigate them through simulation studies. Considering that the variance of these estimators require density estimation, we suggest an alternate approach to obtain the confidence intervals for the residual function. We apply these methods to a breast cancer dataset from National Surgical Adjuvant Breast and Bowel Project (NSABP).

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Quantile residual lifetime with right-censored and length-biased data

Cited by 6 publications

References 33 publications

Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data

Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data

A nonparametric test for equality of survival medians using right-censored prevalent cohort survival data

Inference on quantile residual life for length-biased survival data

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