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A Bayesian functional approach to test models of life course epidemiology over continuous time.
Bodelet, Julien; Potente, Cecilia; Blanc, Guillaume; Chumbley, Justin; Imeri, Hira; Hofer, Scott; Harris, Kathleen Mullan; Muniz-Terrera, Graciela; Shanahan, Michael.
Afiliación
  • Bodelet J; Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland.
  • Potente C; Department of Laboratory Medicine and Pathology, Lausanne University Hospital, Lausanne, Switzerland.
  • Blanc G; Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland.
  • Chumbley J; Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
  • Imeri H; Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland.
  • Hofer S; Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland.
  • Harris KM; Biostatistics and Research Decision Sciences, MSD, Zurich, Switzerland.
  • Muniz-Terrera G; Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland.
  • Shanahan M; Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC, Canada.
Int J Epidemiol ; 53(1)2024 Feb 01.
Article en En | MEDLINE | ID: mdl-38205821
ABSTRACT

BACKGROUND:

Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk.

METHODS:

We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer.

RESULTS:

Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms.

CONCLUSIONS:

The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Insuficiencia Renal Crónica Tipo de estudio: Prognostic_studies / Screening_studies Límite: Female / Humans Idioma: En Revista: Int J Epidemiol Año: 2024 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Insuficiencia Renal Crónica Tipo de estudio: Prognostic_studies / Screening_studies Límite: Female / Humans Idioma: En Revista: Int J Epidemiol Año: 2024 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Reino Unido