Statistical Considerations for Analyzing Data Derived from Long Longitudinal Cohort Studies

Rocío Fernández-Iglesias (), Pablo Martínez-Camblor, Adonina Tardón and Ana Fernández-Somoano
Additional contact information
Rocío Fernández-Iglesias: Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Monforte de Lemos Avenue 3-5, 28029 Madrid, Spain
Pablo Martínez-Camblor: Biomedical Data Science Department, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
Adonina Tardón: Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Monforte de Lemos Avenue 3-5, 28029 Madrid, Spain
Ana Fernández-Somoano: Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Monforte de Lemos Avenue 3-5, 28029 Madrid, Spain

Mathematics, 2023, vol. 11, issue 19, 1-17

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.

Keywords: missing data; quantile regression; tracking; cohort studies; children’s health; cardiovascular risk (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/19/4070/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/19/4070/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:19:p:4070-:d:1247438

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().