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lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition
Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the futur...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059906/ https://www.ncbi.nlm.nih.gov/pubmed/32142521 http://dx.doi.org/10.1371/journal.pone.0228073 |
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author | Plana-Ripoll, Oleguer Canudas-Romo, Vladimir Weye, Nanna Laursen, Thomas M. McGrath, John J. Andersen, Per Kragh |
author_facet | Plana-Ripoll, Oleguer Canudas-Romo, Vladimir Weye, Nanna Laursen, Thomas M. McGrath, John J. Andersen, Per Kragh |
author_sort | Plana-Ripoll, Oleguer |
collection | PubMed |
description | Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available. |
format | Online Article Text |
id | pubmed-7059906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70599062020-03-12 lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition Plana-Ripoll, Oleguer Canudas-Romo, Vladimir Weye, Nanna Laursen, Thomas M. McGrath, John J. Andersen, Per Kragh PLoS One Research Article Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available. Public Library of Science 2020-03-06 /pmc/articles/PMC7059906/ /pubmed/32142521 http://dx.doi.org/10.1371/journal.pone.0228073 Text en © 2020 Plana-Ripoll et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Plana-Ripoll, Oleguer Canudas-Romo, Vladimir Weye, Nanna Laursen, Thomas M. McGrath, John J. Andersen, Per Kragh lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition |
title | lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition |
title_full | lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition |
title_fullStr | lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition |
title_full_unstemmed | lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition |
title_short | lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition |
title_sort | lillies: an r package for the estimation of excess life years lost among patients with a given disease or condition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059906/ https://www.ncbi.nlm.nih.gov/pubmed/32142521 http://dx.doi.org/10.1371/journal.pone.0228073 |
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