The virtues of a deliberately mis-specified disease model in demonstrating a gene-environment interaction

Burstyn, Igor; Kim, Hyang-Mi; Yasui, Yutaka; Cherry, Nicola

doi:10.1136/oem.2008.039081

Cited by 12 publications

(19 citation statements)

References 28 publications

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“…In the analysis of gene–environment effect modification, statistical power becomes an even greater issue as studies typically require large sample sizes to detect effect modification 28. It has been demonstrated that even small errors in the assessment of environmental factors can result in biased interaction parameters and substantially increased sample size requirements for the detection of effect modification 6 7. In our analyses, misclassification of exposure by the JEM as compared to expert assessment resulted in smaller ORs and less likelihood of detecting an effect.…”

Section: Discussionmentioning

confidence: 99%

“…It has been shown that even small errors in the assessment of environmental factors can result in biased estimates of interaction parameters and substantial decreases in study power 6 7. Also, unless we use the most accurate exposure assessment methods in studies evaluating genotypes and environmental exposures, there is a greater probability of detecting associations with genotypes rather than environmental exposures, since genotyping typically has a lower degree of classification error 8…”

Section: Introductionmentioning

confidence: 99%

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Comparison of occupational exposure assessment methods in a case–control study of lead, genetic susceptibility and risk of adult brain tumours

et al. 2010

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Objectives There is great interest in evaluating gene-environment interactions with chemical exposures, but exposure assessment poses a unique challenge in case-control studies. Expert assessment of detailed work history data is usually considered the best approach, but it is a laborious and time-consuming process. We set out to determine if a less intensive method of exposure assessment (a job exposure matrix [JEM]) would produce similar results to a previous analysis that found evidence of effect modification between expert assessed-lead exposure and risk of brain tumors by a single nucleotide polymorphism in the ALAD gene (rs1800435). Methods We used data from a study of 355 patients with glioma, 151 patients with meningioma and 505 controls. Logistic regression models were used to examine associations between brain tumor risk and lead exposure and effect modification by genotype. We evaluated Cohen’s kappa, sensitivity and specificity for the JEM compared to the expert-assessed exposure metrics. Results Although effect estimates were imprecise and driven by a small number of cases, we found evidence of effect modification between lead exposure and ALAD genotype when using expert- but not JEM-derived lead exposure estimates. Kappa values indicated only modest agreement (< 0.5) for the exposure metrics, with the JEM indicating high specificity (~0.9) but poor sensitivity (~0.5). Disagreement between the two methods was generally due to having additional information in the detailed work history. Conclusion These results provide preliminary evidence suggesting that high quality exposure data are likely to improve the ability to detect genetic effect modification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of occupational exposure assessment methods in a case–control study of lead, genetic susceptibility and risk of adult brain tumours

et al. 2010

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“…A slightly different issue, but one that shares some similarities, is how under some conditions gene-only models often more reliably indicate the presence —if not precise magnitude —of a gene–environment interaction than a model with an environmental exposure and gene-by-environment interaction term, in part because only including the gene term avoids potential confounding bias from variables that confound the environmental exposure–outcome association. 47,48 …”

Section: Potential Epidemiologic Advantages Of Proxy Measuresmentioning

confidence: 99%

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

2017

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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.

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“…Inclusion of frequency or duration of use of pesticides in cumulative exposure indices could introduce further misclassification that would typically lead to under-estimates of risk, as has been shown elsewhere. 21 On the other hand, it is also possible that recall of the details of pesticide use over many growing seasons might provide a better estimate of cumulative exposure over a long time period than a biological measurement of exposure from a single application, particularly because urinary levels from non-persistent pesticide exposure reflect only recent use and are not necessarily a measure of long-term use.…”

Section: Discussionmentioning

confidence: 99%

Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study

et al. 2011

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Background The Agricultural Health Study (AHS) is a prospective study of licensed pesticide applicators (largely farmers) and their spouses in Iowa and North Carolina. We evaluate the impact of occupational pesticide exposure misclassification on relative risks using data from the cohort and the AHS Pesticide Exposure Study (AHS/PES). Methods We assessed the impact of exposure misclassification on relative risks using the range of correlation coefficients observed between measured post-application urinary levels of 2,4-dichlorophenoxyacetic acid (2,4-D) and chlorpyrifos metabolite and exposure estimates based on an algorithm from 83 AHS pesticide applications. Results The correlations between urinary levels of 2,4-D and chlorpyrifos metabolite and estimated exposure intensity scores from the expert-derived algorithm were about 0.4 for 2,4-D (n=64), 0.8 for liquid chlorpyrifos (n=4), and 0.6 for granular chlorpyrifos (n=12). Correlations of urinary levels with individual exposure determinants (e.g., kilograms of active ingredient used, duration of application, or number of acres treated) were lower and ranged from −0.36 to 0.19. These findings indicate that scores from an a priori expert-derived algorithm developed for the AHS were more closely related to measured urinary levels than the several individual exposure determinants evaluated here. Estimates of potential bias in relative risks observed in the AHS based on the correlations from the AHS/PES and the proportion of the AHS cohort exposed to various pesticides indicate that nondifferential misclassification of exposure using the algorithm would bias some estimates toward the null, but less than the misclassification associated with individual exposure determinants. Conclusions Based on these correlations and the proportion of the AHS cohort exposed to various pesticides, the potential bias in relative risks from nondifferential exposure misclassification is reduced when exposure estimates are based on an expert algorithm compared to estimates based on separate individual exposure determinants often used in epidemiologic studies. Although correlations between algorithm scores and urinary levels were quite good (i.e., correlations between 0.4 and 0.8), exposure misclassification would still bias relative risk estimates in the AHS towards the null and diminish study power.

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The virtues of a deliberately mis-specified disease model in demonstrating a gene-environment interaction

Abstract: If "gene" contributes to disease risk only in the presence of exposure, the existence of the gene-environment interaction can be efficiently inferred from a deliberately mis-specified "gene-only" disease model in nested case-control studies.

Cited by 12 publications

References 28 publications

Comparison of occupational exposure assessment methods in a case–control study of lead, genetic susceptibility and risk of adult brain tumours

Comparison of occupational exposure assessment methods in a case–control study of lead, genetic susceptibility and risk of adult brain tumours

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

Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study

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