Quantiles of residual survival

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In reliability theory, the lifetime remaining in a network of components after an initial run-in period is an important property of the system. Similarly, for medical interventions residual survival characterizes the subsequent experience of patients who survive beyond the beginning of follow-up. Here we show how quantiles of the residual survival distribution can be used to provide such a characterization. We first discuss properties of the residual quantile function and its close relationship to the hazard function.We then consider parametric estimation of the residual quantile function, focusing on the generalized gamma distribution. Finally, we describe an application of quantiles of residual survival to help describe the effects at the population level of the introduction and sustained use of highly active antiretroviral therapy for the treatment of HIV/AIDS.

Original languageEnglish (US)
Title of host publicationRisk Assessment and Evaluation of Predictions
EditorsAxel Gandy, Glen Satten, Mitchell Gail, Ruth Pfeiffer, Tianxi Cai, Mei-Ling Ting Lee
PublisherSpringer Science and Business Media, LLC
Pages87-103
Number of pages17
ISBN (Print)9781461489801
DOIs
StatePublished - Jan 1 2013
EventInternational conference on Risk Assessment and Evaluation of Predictions, 2011 - Silver Spring, United States
Duration: Oct 12 2011Oct 14 2011

Publication series

NameLecture Notes in Statistics
Volume215
ISSN (Print)0930-0325
ISSN (Electronic)2197-7186

Other

OtherInternational conference on Risk Assessment and Evaluation of Predictions, 2011
CountryUnited States
CitySilver Spring
Period10/12/1110/14/11

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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  • Cite this

    Cox, C., Schneider, M. F., & Muñoz, A. (2013). Quantiles of residual survival. In A. Gandy, G. Satten, M. Gail, R. Pfeiffer, T. Cai, & M-L. T. Lee (Eds.), Risk Assessment and Evaluation of Predictions (pp. 87-103). (Lecture Notes in Statistics; Vol. 215). Springer Science and Business Media, LLC. https://doi.org/10.1007/978-1-4614-8981-8_6