351: Attenuation in Risk Estimates in Logistic and Cox Proportional-Hazards Models Due to Group-Based Exposure Assessment Strategy

Kim, H M; Yasui, Yutaka; Cherry, Nicola; Burstyn, Igor

doi:10.1093/aje/161.supplement_1.s88b

Cited by 6 publications

(7 citation statements)

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“…Self-reported data, collected through questionnaire typically involve recall error that is usually assumed to be of the classical type [5]. Also, grouped-exposure data obtained by assigning the same value of the unobserved exposure variable (e. g. air pollution or radiation dose) to individuals sharing some characteristics are often assumed to be contaminated with Berkson error [6]. In many other situations, the covariate data may be affected by a mixture of errors of both classical and Berkson types.…”

Section: Introductionmentioning

confidence: 99%

“…The impact of the classical error in the covariates of a Cox regression model has been well-documented and includes the attenuation of the covariate effect [8,9]. Moreover, a few references have reported that the Berkson error may also have a similar effect [6,10]. However, the effect of a mixture of classical and Berkson errors in the covariates of a proportional hazards model still remains to be well investigated.…”

Section: Introductionmentioning

confidence: 99%

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Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Tapsoba

Chao

Wang

2019

The International Journal of Biostatistics

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Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Tapsoba

Chao

Wang

2019

The International Journal of Biostatistics

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“…Sensitivity analyses were conducted with alternative simulated employment durations based on information on employment durations from another, partly overlapping, cohort of British rubber factory workers6 15 29 and showed that results of the main analyses are robust. Although exposure estimates from JEMs are generated from mean exposure values and therefore do not allow for individual variabilities, errors that arise from them tend to be attenuated Berkson-type errors, which, unlike random errors, generally do not bias exposure–response associations 30–32. Finally, exposure-specific estimates of LCE enabled multipollutant models to explore issues of complex exposure mixtures across the production process.…”

Section: Discussionmentioning

confidence: 99%

Lifetime cumulative exposure to rubber dust, fumes and N-nitrosamines and non-cancer mortality: a 49-year follow-up of UK rubber factory workers

Hidajat

McElvenny²,

Ritchie³

et al. 2020

Occup Environ Med

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ObjectivesTo examine associations between occupational exposures to rubber dust, rubber fumes and N-nitrosamines and non-cancer mortality.MethodsA cohort of 36 441 males aged 35+ years employed in British rubber factories was followed-up to 2015 (94% deceased). Competing risk survival analysis was used to assess risks of dying from non-cancer diseases (respiratory, urinary, cerebrovascular, circulatory and digestive diseases). Occupational exposures to rubber dust, rubber fumes, N-nitrosamines were derived based on a population-specific quantitative job-exposure matrix which in-turn was based on measurements in the EU-EXASRUB database.ResultsExposure–response associations of increased risk with increasing exposure were found for N-nitrosomorpholine with mortality from circulatory diseases (subdistribution hazard ratio (SHR) 1.17; 95% CI 1.12 to 1.23), ischaemic heart disease (IHD) (SHR 1.19; 95% CI 1.13 to 1.26), cerebrovascular disease (SHR 1.19; 95% CI 1.07 to 1.32) and exposures to N-nitrosodimethylamine with respiratory disease mortality (SHR 1.41; 95% CI 1.30 to 1.53). Increased risks for mortality from circulatory disease, IHD and digestive diseases were found with higher levels of exposures to rubber dust, rubber fumes and N-nitrosamines sum, without an exposure-dependent manner. No associations were observed between rubber dust, rubber fumes and N-nitrosamines exposures with mortality from asthma, urinary disease, bronchitis, emphysema, liver disease and some digestive diseases.ConclusionsIn a cohort of rubber factory workers with 49 years of follow-up, increased risk for mortality from circulatory, cerebrovascular, respiratory and digestive diseases were found to be associated with cumulative occupational exposures to specific agents.

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“…Our findings are relevant to studies with binary and time-to-event outcomes, although caution is required in drawing analogies. For example, when ρ=0 and we are reduced to Berkson-type error, with Cox proportional hazard model is nuanced with bias depending on rarity of censoring [32,33].…”

Section: Discussionmentioning

confidence: 99%

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

Burstyn¹,

Barone-Adesi²,

Vocht³

et al. 2019

Preprint

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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. To make reliable inference about the magnitude of effect of cumulative exposure on the outcome, we can use external information on the relationship between duration and intensity of exposure even if intensity of exposure is not available at the individual level.

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351: Attenuation in Risk Estimates in Logistic and Cox Proportional-Hazards Models Due to Group-Based Exposure Assessment Strategy

Cited by 6 publications

References 0 publications

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Lifetime cumulative exposure to rubber dust, fumes and N-nitrosamines and non-cancer mortality: a 49-year follow-up of UK rubber factory workers

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

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