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Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correla...

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Detalles Bibliográficos
Autores principales: Gecili, Emrah, Palipana, Anushka, Brokamp, Cole, Huang, Rui, Andrinopoulou, Eleni-Rosalina, Pestian, Teresa, Rasnick, Erika, Keogh, Ruth H., Ni, Yizhao, Clancy, John P., Ryan, Patrick, Szczesniak, Rhonda D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374306/
https://www.ncbi.nlm.nih.gov/pubmed/34434833
http://dx.doi.org/10.1016/j.mex.2021.101313
Descripción
Sumario:This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows. • This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context. • Steps required for mediation analyses in the seasonality model are shown. • The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study.