A Bayesian approach to comparing common models of life-course epidemiology | Zendy

Justin Chumbley | Zendy; Weijie Xu | Zendy; Cecilia Potente | Zendy; Kathleen Mullan Harris | Zendy; Michael J. Shanahan | Zendy

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A Bayesian approach to comparing common models of life-course epidemiology

Author(s) -

Justin Chumbley,

Weijie Xu,

Cecilia Potente,

Kathleen Mullan Harris,

Michael J. Shanahan

Publication year - 2021

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyab073

Subject(s) - life course approach , bayesian probability , econometrics , computer science , statistics , missing data , bayesian inference , artificial intelligence , psychology , machine learning , mathematics , developmental psychology

Life-course epidemiology studies people's health over long periods, treating repeated measures of their experiences (usually risk factors) as predictors or causes of subsequent morbidity and mortality. Three hypotheses or models often guide the analyst in assessing these sequential risks: the accumulation model (all measurement occasions are equally important for predicting the outcome), the critical period model (only one occasion is important) and the sensitive periods model (a catch-all model for any other pattern of temporal dependence).

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