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Journal article
Estimators for longitudinal latent exposure models: examining measurement model assumptions
Statistics in medicine, v 36(13), pp 2048-2066
15 Jun 2017
PMID: 28239905
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time‐changing longitudinal exposure have appreciable impact on (maximum‐likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure‐disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time‐changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd.
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Details
- Title
- Estimators for longitudinal latent exposure models: examining measurement model assumptions
- Creators
- Brisa N. Sánchez - University of MichiganSehee Kim - University of MichiganMary D. Sammel - University of Pennsylvania
- Publication Details
- Statistics in medicine, v 36(13), pp 2048-2066
- Publisher
- Wiley
- Number of pages
- 19
- Grant note
- NIEHS (P01 ES012874; P42 ES05947; R01 ES007821; R01 ES007821; P01 ES022844; R01 ES016932; R01 ES017022; 1-P20-ES018171-01) ELEMENT investigators
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000402797900004
- Scopus ID
- 2-s2.0-85013838808
- Other Identifier
- 991020100082104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability