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Longitudinal change in physical functioning and dropout due to death among the oldest old: a comparison of three methods of analysis

Author

Listed:
  • Jani Raitanen

    (Tampere University
    UKK Institute for Health Promotion Research)

  • Sari Stenholm

    (Tampere University
    University of Turku and Turku University Hospital
    University of Turku and Turku University Hospital)

  • Kristina Tiainen

    (Tampere University
    Tampere University)

  • Marja Jylhä

    (Tampere University
    Tampere University
    Science Center of Tampere University Hospital)

  • Jaakko Nevalainen

    (Tampere University)

Abstract

Longitudinal studies examining changes in physical functioning with advancing age among very old people are plagued by high death rates, which can lead to biased estimates. This study was conducted to analyse changes in physical functioning among the oldest old with three distinct methods which differ in how they handle dropout due to death. The sample consisted of 3992 persons aged 90 or over in the Vitality 90+ Study who were followed up on average for 2.5 years (range 0–13 years). A generalized estimating equation (GEE) with independent ‘working’ correlation, a linear mixed-effects (LME) model and a joint model consisting of longitudinal and survival submodels were used to estimate the effect of age on physical functioning over 13 years of follow-up. We observed significant age-related decline in physical functioning, which furthermore accelerated significantly with age. The average rate of decline differed markedly between the models: the GEE-based estimate for linear decline among survivors was about one-third of the average individual decline in the joint model and half the decline indicated by the LME model. In conclusion, the three methods yield substantially different views on decline in physical functioning: the GEE model may be useful for considering the effect of intervention measures on the outcome among living people, whereas the LME model is biased regarding studying outcomes associated with death. The joint model may be valuable for predicting the future characteristics of the oldest old and planning elderly care as life expectancy continues gradually to rise.

Suggested Citation

  • Jani Raitanen & Sari Stenholm & Kristina Tiainen & Marja Jylhä & Jaakko Nevalainen, 2020. "Longitudinal change in physical functioning and dropout due to death among the oldest old: a comparison of three methods of analysis," European Journal of Ageing, Springer, vol. 17(2), pages 207-216, June.
    • Handle: RePEc:spr:eujoag:v:17:y:2020:i:2:d:10.1007_s10433-019-00533-x
    DOI: 10.1007/s10433-019-00533-x
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    References listed on IDEAS

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    1. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    2. Rizopoulos, Dimitris, 2010. "JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i09).
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    1. Susanne Iwarsson & Marja J. Aartsen & Morten Wahrendorf & Matthias Kliegel, 2021. "What will the horrible year of 2020 bring to the future of ageing research?," European Journal of Ageing, Springer, vol. 18(1), pages 1-3, March.

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