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Bayesian correction for measurement error following group-based exposure assessment in a case-referent study
Journal article   Open access   Peer reviewed

Bayesian correction for measurement error following group-based exposure assessment in a case-referent study

Igor Burstyn, Frank de Vocht, Hyang-Mi Kim and Nicola Cherry
Occupational and environmental medicine (London, England), v 68(Suppl 1), pp A44-A44
Sep 2011
DOI: https://doi.org/10.1136/oemed-2011-100382.145
url
https://doi.org/10.1136/oemed-2011-100382.145View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open

Abstract

Objectives We applied Bayesian analysis to case-referent data on occupational noise exposure and death from ischaemic heart disease (IHD) and contrast analyses with and without correction for measurement error in group-level noise exposure estimates. Methods A 1:1 matched case-referent study nested in an industrial cohort in England resulted in 117 matched sets; 7225 area noise measurements in dBA from 215 buildings were the basis of modeling building-specific average exposures during the decade of in service IHD death. An additive quasi-Berkson error model was assumed. Bayesian analysis was conducted under varying assumptions about magnitude of error (with SD of error (SDe) up to 10 dBA) and a prior strength of hypothesis (flat vs informative -- 98% range (1.00,1.02) -- prior on OR). All analyses ignored matching and were conducted without adjustment for confounders to estimate log(OR)/dBA in a logistic disease model. Results Analysis not corrected for measurement error with flat prior yielded OR 0.99 (95% CrI 0.96–1.02). With flat prior on OR with measurement error correction, OR had 95% CrI 0.97–1.02; with informative prior on the association, the corresponding OR is 1.01, 95% CrI 1.00–1.02, same as prior. The posterior distribution of SDe had median 1.6 (95% CrI 1.2–2.0) dBA. Conclusions Measurement error did not bias the uncorrected results. Conditional logistic regression with adjustment for confounders is congruent with Bayesian analysis (115 pairs, OR 0.98, 95% CI 0.94 to 1.02). Analysis provided insights into plausible magnitudes of measurement error in the study. Extension of this methodology to consider matching, confounders and retrospective nature of data is required.

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