Epidemiologic investigations of associations between protracted low level occupational exposures and cancer mortality routinely encounter the following problems: 1) potential latency effects between exposure and disease;2) potential bias resulting from exposure measurement error;and, 3) potential bias resulting from health-related selection out of employment (i.e., the healthy worker survivor effect). The identified problems are of direct relevance to worker protection, as each is a source of bias that may lead to spurious conclusions about the adverse effects of occupational hazards. The goal of this project is to improve the analytical tools available to address these problems. We will develop a conceptual description of each problem, develop a simple analytical tool (or tools) to reduce or eliminate the potential bias, evaluate the proposed analytical method via simulation analyses, and then illustrate the application of the proposed method using empirical data. We will begin by exploring the use of flexible latency models for occupational cancer studies. Via simulation analyses we will evaluate the use of these flexible models for reducing bias due to mis-specification of exposure lag assumptions;and, in empirical analyses of rubber hydrochloride and asbestos textile worker cohort data we will illustrate the application of these methods. Next, we will develop an approach to control for bias that can arise when grouped data are used to assign exposure scores, as in a job-exposure matrix. The use of assigned exposure values is often assumed to result in a Berkson error model that does not produce biased risk estimates. Using simulated data, we will evaluate the conditions under which the Berkson model applies, and develop approaches to exploit this error model to reduce bias. We will illustrate these approaches with empirical data from a cohort study of electrical utility workers. Finally, we will identify the conditions under which non-standard regression methods (e.g., G-estimation) are necessary in cohort studies to control for the healthy worker survivor effect. We will use simulation methods to explore these conditions, and develop simple analytical tools to guide investigators on when to use G-estimation. The proposal addresses the NORA priority area on cancer research methods. The results of the proposed research will further improve the analytic methods used in occupational cancer studies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA117841-03
Application #
7540461
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Martin, Damali
Project Start
2007-01-01
Project End
2009-12-31
Budget Start
2009-01-01
Budget End
2009-12-31
Support Year
3
Fiscal Year
2009
Total Cost
$173,291
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Keil, Alexander P; Edwards, Jessie K; Richardson, David B et al. (2014) The parametric g-formula for time-to-event data: intuition and a worked example. Epidemiology 25:889-97
Edwards, Jessie K; Cole, Stephen R; Chu, Haitao et al. (2014) Accounting for outcome misclassification in estimates of the effect of occupational asbestos exposure on lung cancer death. Am J Epidemiol 179:641-7
Naimi, Ashley I; Cole, Stephen R; Hudgens, Michael G et al. (2014) Estimating the effect of cumulative occupational asbestos exposure on time to lung cancer mortality: using structural nested failure-time models to account for healthy-worker survivor bias. Epidemiology 25:246-54
Edwards, Jessie K; McGrath, Leah J; Buckley, Jessie P et al. (2014) Occupational radon exposure and lung cancer mortality: estimating intervention effects using the parametric g-formula. Epidemiology 25:829-34
Cole, Stephen R; Richardson, David B; Chu, Haitao et al. (2013) Analysis of occupational asbestos exposure and lung cancer mortality using the g formula. Am J Epidemiol 177:989-96
Zhang, Jing; Cole, Stephen R; Richardson, David B et al. (2013) A Bayesian approach to strengthen inference for case-control studies with multiple error-prone exposure assessments. Stat Med 32:4426-37
Naimi, Ashley I; Cole, Stephen R; Hudgens, Michael G et al. (2013) Assessing the component associations of the healthy worker survivor bias: occupational asbestos exposure and lung cancer mortality. Ann Epidemiol 23:334-41
Richardson, David B; Wing, Steve; Cole, Stephen R (2013) Missing doses in the life span study of Japanese atomic bomb survivors. Am J Epidemiol 177:562-8
Edwards, Jessie K; Cole, Stephen R; Troester, Melissa A et al. (2013) Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation data. Am J Epidemiol 177:904-12
Richardson, David B; Cole, Stephen R; Chu, Haitao (2013) Random effects regression models for trends in standardised mortality ratios. Occup Environ Med 70:133-9

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