Lillies: An R package for the estimation of excess life years lost among patients with a given disease or condition

Oleguer Plana-Ripoll*, Vladimir Canudas-Romo, Nanna Weye, Thomas M. Laursen, John J. McGrath, Per Kragh Andersen

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    32 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article numbere0228073
    JournalPLoS ONE
    Volume15
    Issue number3
    DOIs
    Publication statusPublished - 2020

    Fingerprint

    Dive into the research topics of 'Lillies: An R package for the estimation of excess life years lost among patients with a given disease or condition'. Together they form a unique fingerprint.

    Cite this