A comparison of Bayesian hierarchical modeling with group‐based exposure assessment in occupational epidemiology

Li, Xing; Burstyn, Igor; Richardson, David B.; Gustafson, Paul

doi:10.1002/sim.5791

Cited by 7 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With such data one can use established methods to correct for bias to the null from measurement error. 12,13,65,66 The fact that this can be done is another argument for using more proxy exposure estimates: the drawback of bias to the null from exposure measurement error may be more easily addressed than the drawback with more personal exposure measures of needing to control for additional confounding or reverse causation biases that may not even be recognized. Furthermore, with the more proxy exposure estimate there is the possibility of using the correlation with more personal exposure in IV analyses.…”

Section: Discussionmentioning

confidence: 99%

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

2017

View full text Add to dashboard Cite

The technological ability to make personal measurements of toxicant exposures is growing rapidly. While this can decrease measurement error and therefore help reduce attenuation of effect estimates, we argue that as measures of exposure or dose become more personal, threats to validity of study findings can increase in ways that more proxy measures may avoid. We use Directed Acyclic Graphs (DAGs) to describe conditions where confounding is introduced by use of more personal measures of exposure and avoided via more proxy measures of personal exposure or target tissue dose. As exposure or dose estimates are more removed from the individual, they become less susceptible to biases from confounding by personal factors that can often be hard to control, such as personal behaviors. Similarly, more proxy exposure estimates are less susceptible to reverse causation. We provide examples from the literature where adjustment for personal factors in analyses that use more proxy exposure estimates have little effect on study results. In conclusion, increased personalized exposure assessment has important advantages for measurement accuracy, but it can increase the possibility of biases from personal factors and reverse causation compared with more proxy exposure estimates. Understanding the relation between more and less proxy exposures, and variables that could introduce confounding are critical components to study design.

show abstract

Section: Discussionmentioning

confidence: 99%

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

2017

View full text Add to dashboard Cite

show abstract

“…Modeling made possible through distributions of probability amid uncertainties, provided by the BNs, to become appropriate to model the occupational risks, considering that it allows inference from hypotheses and on the relationship between risk factors. However, the risk is a changeable data when it regards to time (among other aspects), and modeling through the BNs satisfies this logic, as it stimulates stochastic processes (Englehardt et al, 2003;Hsu et al, 2014;Puncher et al, 2013;Xing et al, 2013).…”

Section: Stage Iii: Structural Proposal Of Application Of the Bnsmentioning

confidence: 99%

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Vieira

Silva

2017

Prod.

View full text Add to dashboard Cite

“…At best, in most retrospective epidemiological studies researchers have information on the (historic) distribution of exposure intensity, but not individual values. In occupational epidemiology, this led to development of practice and theory of job-exposure matrices [12,13] and group-based exposure assessment [14,15,16]. However, such approaches raise the question of how to proceed with the analysis of health impact of accumulated exposure, when duration is assessed on an individual level (e.g., via questionnaires), while exposure intensity is subject to various modeling assumptions, given that individual-level assessment of exposure intensity is rarely possible (e.g., self-reports of exposure are not reliable, individual exposure measurements are almost always not available).…”

Section: Introductionmentioning

confidence: 99%

What to Do When Accumulated Exposure Affects Health but Only Its Duration Was Measured? A Case of Linear Regression

Burstyn

Barone-Adesi²,

Vocht³

et al. 2019

IJERPH

Self Cite

View full text Add to dashboard Cite

Background: We considered a problem of inference in epidemiology when cumulative exposure is the true dose metric for disease, but investigators are only able to measure its duration on each subject. Methods: We undertook theoretical analysis of the problem in the context of a continuous response caused by cumulative exposure, when duration and intensity of exposure follow log-normal distributions, such that analysis by linear regression is natural. We present a Bayesian method to adjust duration-only analysis to incorporate partial knowledge about the relationship between duration and intensity of exposure and illustrate this method in the context of association of smoking and lung function. Results: We derive equations that (a) describe under what circumstances bias arises when duration of exposure is used as a proxy of cumulative exposure, (b) quantify the degree of such bias and loss of precision, and (c) describe how knowledge about relationship of duration and intensity of exposure can be used to recover an estimate of the effect of cumulative exposure when only duration was observed on every subject. Conclusions: Under our assumptions, when duration and intensity of exposure are either independent or positively correlated, we can be more confident in qualitatively interpreting the direction of effects that arise from use of duration of exposure per se. We can use external information on the relationship between duration and intensity of exposure (namely: correlation and variance of intensity), even if intensity of exposure is not available at the individual level, to make reliable inferences about the magnitude of effect of cumulative exposure on the outcome.

show abstract

A comparison of Bayesian hierarchical modeling with group‐based exposure assessment in occupational epidemiology

Cited by 7 publications

References 23 publications

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

What to Do When Accumulated Exposure Affects Health but Only Its Duration Was Measured? A Case of Linear Regression

Contact Info

Product

Resources

About