Layman's abstract: Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data

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  • Author: Yixin Wang Zhefang Zhou Xiao‐Hua Zhou Yong Zhou
  • Date: 16 May 2018
  • Copyright: © Statistical Society of Canada

Every Wednesday and Friday on Statistics Views, we publish layman's abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format. This article featured today is from The Canadian Journal of Statistics: Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data by Yixin Wang Zhefang Zhou Xiao‐Hua Zhou Yong Zhou.

Read the layman's abstract below.

Yixin Wang Zhefang Zhou Xiao‐Hua Zhou Yong Zhou (2017), Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data, Applied Stochastic Models in Business and Industry, 45, pages 220–250, doi: 10.1002/cjs.11319

thumbnail image: Layman's abstract: Nonparametric and semiparametric estimation of quantile residual lifetime for length‐biased and right‐censored data

Residual life, which means the remaining lifetime of a subject or a unit who has already survived to some time, is important in survival analysis. For example, patients are always concerned with their disease progression, including how many years they can live after the receipt of a treatment, especially for those who suffer from a chronic or irreversible disease like dementia. However, accompanying with skyrocketing costs needed for increased dementia patients, no effective methods have been developed to treat and prevent dementia so far, so exact estimation of the residual lifetime of people who suffering from dementia is urgent and meaningful. On the other hand, complex data often occur in medical research and further complicate the estimation. This paper considered the length-biased and right-censored data, and proposed new statistical methods to estimate the quantile residual lifetime based on the special structure of the data. And they further studied asymptotic properties of the estimator and two-sample comparison problems. Moreover, their methods are used to analyze the quantile residual lifetime of subjects diagnosed with dementia from the National Alzheimer’s Coordinating Center. This is an interesting work, which can help physicians design reasonable treatment schedule, patients make suitable plans to improve the quality of life, and government better allocate budget.

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