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Index or illusion: The case of frailty indices in the Health and Retirement Study

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  • Yi-Sheng Chao
  • Hsing-Chien Wu
  • Chao-Jung Wu
  • Wei-Chih Chen

Abstract

Introduction: Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality. Methods: Three FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality. Results: We found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs. Conclusion: FIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

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  • Yi-Sheng Chao & Hsing-Chien Wu & Chao-Jung Wu & Wei-Chih Chen, 2018. "Index or illusion: The case of frailty indices in the Health and Retirement Study," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    • Handle: RePEc:plo:pone00:0197859
    DOI: 10.1371/journal.pone.0197859
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    References listed on IDEAS

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    1. Yi-Sheng Chao & Chao-Jung Wu, 2017. "Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-20, September.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
    4. Calcagno, Vincent & de Mazancourt, Claire, 2010. "glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i12).
    5. F. Thomas Juster & Richard Suzman, 1995. "An Overview of the Health and Retirement Study," Journal of Human Resources, University of Wisconsin Press, vol. 30, pages 7-56.
    6. Moon, J. Robin & Glymour, M. Maria & Subramanian, S.V. & Avendaño, Mauricio & Kawachi, Ichiro, 2012. "Transition to retirement and risk of cardiovascular disease: Prospective analysis of the US health and retirement study," Social Science & Medicine, Elsevier, vol. 75(3), pages 526-530.
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