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Statistical Considerations for Analyzing Data Derived from Long Longitudinal Cohort Studies

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  • Rocío Fernández-Iglesias

    (Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Monforte de Lemos Avenue 3-5, 28029 Madrid, Spain
    University Institute of Oncology of the Principality of Asturias (IUOPA)—Department of Medicine, University of Oviedo, Julian Clavería Street s/n, 33006 Oviedo, Asturias, Spain
    Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Roma Avenue s/n, 33001 Oviedo, Asturias, Spain)

  • Pablo Martínez-Camblor

    (Biomedical Data Science Department, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
    Faculty of Health Sciences, Universidad Autonoma de Chile, Providencia 7500912, Chile)

  • Adonina Tardón

    (Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Monforte de Lemos Avenue 3-5, 28029 Madrid, Spain
    University Institute of Oncology of the Principality of Asturias (IUOPA)—Department of Medicine, University of Oviedo, Julian Clavería Street s/n, 33006 Oviedo, Asturias, Spain
    Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Roma Avenue s/n, 33001 Oviedo, Asturias, Spain)

  • Ana Fernández-Somoano

    (Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Monforte de Lemos Avenue 3-5, 28029 Madrid, Spain
    University Institute of Oncology of the Principality of Asturias (IUOPA)—Department of Medicine, University of Oviedo, Julian Clavería Street s/n, 33006 Oviedo, Asturias, Spain
    Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Roma Avenue s/n, 33001 Oviedo, Asturias, Spain)

Abstract

Modern science is frequently based on the exploitation of large volumes of information storage in datasets and involving complex computational architectures. The statistical analyses of these datasets have to cope with specific challenges and frequently involve making informed but arbitrary decisions. Epidemiological papers have to be concise and focused on the underlying clinical or epidemiological results, not reporting the details behind relevant methodological decisions. In this work, we used an analysis of the cardiovascular-related measures tracked in 4–8-year-old children, using data from the INMA-Asturias cohort for illustrating how the decision-making process was performed and its potential impact on the obtained results. We focused on two particular aspects of the problem: how to deal with missing data and which regression model to use to evaluate tracking when there are no defined thresholds to categorize variables into risk groups. As a spoiler, we analyzed the impact on our results of using multiple imputation and the advantage of using quantile regression models in this context.

Suggested Citation

  • Rocío Fernández-Iglesias & Pablo Martínez-Camblor & Adonina Tardón & Ana Fernández-Somoano, 2023. "Statistical Considerations for Analyzing Data Derived from Long Longitudinal Cohort Studies," Mathematics, MDPI, vol. 11(19), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4070-:d:1247438
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    References listed on IDEAS

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    1. 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).
    2. Oconnor, Christopher, 2023. "Robust estimates of vulnerability to poverty using quantile models," Economic Modelling, Elsevier, vol. 123(C).
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