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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.
Afiliação
  • Plana-Ripoll O; National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
  • Canudas-Romo V; School of Demography, ANU College of Arts & Social Sciences, Australian National University, Canberra, Australia.
  • Weye N; National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
  • Laursen TM; National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
  • McGrath JJ; National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
  • Andersen PK; Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.
PLoS One ; 15(3): e0228073, 2020.
Article em En | MEDLINE | ID: mdl-32142521
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Interpretação Estatística de Dados / Expectativa de Vida / Causas de Morte Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Dinamarca

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Interpretação Estatística de Dados / Expectativa de Vida / Causas de Morte Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Dinamarca