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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...

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Autores principales: Plana-Ripoll, Oleguer, Canudas-Romo, Vladimir, Weye, Nanna, Laursen, Thomas M., McGrath, John J., Andersen, Per Kragh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
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.
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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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