ArticleAmerican journal of epidemiology2011
Hierarchical latency models for dose-time-response associations.
Article in American journal of epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ESTIMATION AND INFERENCE FOR EXPOSURE EFFECTS WITH LATENCY IN THE COX PROPORTIONAL HAZARDS MODEL IN THE PRESENCE OF EXPOSURE MEASUREMENT ERROR.The annals of applied statistics · 2023Article
- Latency estimation for chronic disease risk: a damped exponential weighting model.European journal of epidemiology · 2020Article
- A Bayesian approach to investigate life course hypotheses involving continuous exposures.International journal of epidemiology · 2018Article
- Environmental exposure mixtures: questions and methods to address them.Current epidemiology reports · 2018Article
- A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes.International journal of environmental research and public health · 2017Article
- Quantifying risk over the life course - latency, age-related susceptibility, and other time-varying exposure metrics.Statistics in medicine · 2016Article
- Glucocorticoid prescriptions and breast cancer recurrence: a Danish nationwide prospective cohort study.Annals of oncology : official journal of the European Society for Medical Oncology · 2014Article
- Markov chain Monte Carlo: an introduction for epidemiologists.International journal of epidemiology · 2013Article
- On the proportional hazards model for occupational and environmental case-control analyses.BMC medical research methodology · 2013Article
- Model averaging in the analysis of leukemia mortality among Japanese A-bomb survivors.Radiation and environmental biophysics · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Exposure lagging and exposure-time window analysis are 2 widely used approaches to allow for induction and latency periods in analyses of exposure-disease associations. Exposure lagging implies a strong parametric assumption about the temporal evolution of the exposure-disease association. An exposure-time window analysis allows for a more flexible description of temporal variation in exposure effects but may result in unstable risk estimates that are sensitive to how windows are defined. The authors describe a hierarchical regression approach that combines time window analysis with a parametric latency model. They illustrate this approach using data from 2 occupational cohort studies: studies of lung cancer mortality among 1) asbestos textile workers and 2) uranium miners. For each cohort, an exposure-time window analysis was compared with a hierarchical regression analysis with shrinkage toward a simpler, second-stage parametric latency model. In each cohort analysis, there is substantial stability gained in time window-specific estimates of association by using a hierarchical regression approach. The proposed hierarchical regression model couples a time window analysis with a parametric latency model; this approach provides a way to stabilize risk estimates derived from a time window analysis and a way to reduce bias arising from misspecification of a parametric latency model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.