Grant Number:	2R01CA117841-04 Interpret this number
Primary Investigator:	Richardson, David
Organization:	Univ Of North Carolina Chapel Hill
Project Title:	Cohort Analysis Methods for Occupational Cancer Studies
Fiscal Year:	2010

DESCRIPTION (provided by applicant): 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. 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 research is to accelerate the development and dissemination of innovative analytical tools to reduce bias and increase precision of risk estimates derived from cohort studies. Leveraging work in our initial project, we draw upon the insights into each problem developed and demonstrate how solutions can be found via analogies to state-of-the-art methods applied in other research areas. We can quickly translate these methods for application to our purposes and apply them in cohort analyses to illustrate their utility. We will demonstrate how the standard approach to exposure time-window analysis may be coupled with a second stage parametric latency model to reduce bias and improve precision of estimates of exposure-time-response associations. During the initial project we developed an innovative method to directly fit a parametric latency model in a simple (single stage) regression model. Hierarchical regression methods have been applied in other research areas for smoothing of parametric functions and shown to yield notable gains in the accuracy of effect estimates. Next, we will develop an approach to correctly account for uncertainty in exposure estimates derived via a job-exposure-matrix (JEM). Recently, investigators have used exposure simulation approaches to generate 'realizations' of exposure scores sampled from underlying distributions. We will demonstrate that the exposure simulation approach may induce attenuation bias in estimates of exposure-disease associations and how Bayesian methods may be coupled with JEMs to provide an intuitive framework for handling uncertainty in exposure estimates without introducing attenuation bias. Finally, we will develop a readily-implementable approach for fitting structural nested models that provide estimates of occupational exposure-cancer associations that are not biased by the healthy worker survivor effect. During the initial project, we assessed bias in exposure-mortality associations in order to explore conditions under which standard regression methods are inadequate. This work showed that there are many settings in which standard regression analysis yielded strongly biased effect measures. Drawing upon methods for structural nested models recently applied in infectious disease epidemiology, we will demonstrate how regression models can be fitted to produce estimates of association that are unbiased by the HSWE. The approach that we will develop produces standard effect measures and overcomes many limitations of prior applications of G-methods. This research will improve the methods used in occupational cancer studies. PUBLIC HEALTH RELEVANCE: The goal of the proposed research, which is a competing continuation of R01-CA117841 Cohort Analysis Methods for Occupational Cancer Studies, is to accelerate the development and dissemination of innovative analytical tools to improve the analysis of occupational cohort studies of cancer.




The Parametric G-formula For Time-to-event Data: Intuition And A Worked Example
Authors: Keil A.P. , Edwards J.K. , Richardson D.B. , Naimi A.I. , Cole S.R. .
Source: Epidemiology (cambridge, Mass.), 2014 Nov; 25(6), p. 889-97.
PMID: 25140837
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Occupational Radon Exposure And Lung Cancer Mortality: Estimating Intervention Effects Using The Parametric G-formula
Authors: Edwards J.K. , McGrath L.J. , Buckley J.P. , Schubauer-Berigan M.K. , Cole S.R. , Richardson D.B. .
Source: Epidemiology (cambridge, Mass.), 2014 Nov; 25(6), p. 829-34.
PMID: 25192403
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Accounting For Outcome Misclassification In Estimates Of The Effect Of Occupational Asbestos Exposure On Lung Cancer Death
Authors: Edwards J.K. , Cole S.R. , Chu H. , Olshan A.F. , Richardson D.B. .
Source: American Journal Of Epidemiology, 2014-03-01 00:00:00.0; 179(5), p. 641-7.
PMID: 24352593
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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
Authors: Naimi A.I. , Cole S.R. , Hudgens M.G. , Richardson D.B. .
Source: Epidemiology (cambridge, Mass.), 2014 Mar; 25(2), p. 246-54.
PMID: 24487207
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Causal Inference In Occupational Epidemiology: Accounting For The Healthy Worker Effect By Using Structural Nested Models
Authors: Naimi A.I. , Richardson D.B. , Cole S.R. .
Source: American Journal Of Epidemiology, 2013-12-15 00:00:00.0; 178(12), p. 1681-6.
PMID: 24077092
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A Bayesian Approach To Strengthen Inference For Case-control Studies With Multiple Error-prone Exposure Assessments
Authors: Zhang J. , Cole S.R. , Richardson D.B. , Chu H. .
Source: Statistics In Medicine, 2013-11-10 00:00:00.0; 32(25), p. 4426-37.
PMID: 23661263
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Assessing The Component Associations Of The Healthy Worker Survivor Bias: Occupational Asbestos Exposure And Lung Cancer Mortality
Authors: Naimi A.I. , Cole S.R. , Hudgens M.G. , Brookhart M.A. , Richardson D.B. .
Source: Annals Of Epidemiology, 2013 Jun; 23(6), p. 334-41.
PMID: 23683709
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Analysis Of Occupational Asbestos Exposure And Lung Cancer Mortality Using The G Formula
Authors: Cole S.R. , Richardson D.B. , Chu H. , Naimi A.I. .
Source: American Journal Of Epidemiology, 2013-05-01 00:00:00.0; 177(9), p. 989-96.
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Accounting For Misclassified Outcomes In Binary Regression Models Using Multiple Imputation With Internal Validation Data
Authors: Edwards J.K. , Cole S.R. , Troester M.A. , Richardson D.B. .
Source: American Journal Of Epidemiology, 2013-05-01 00:00:00.0; 177(9), p. 904-12.
PMID: 24627573
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Missing Doses In The Life Span Study Of Japanese Atomic Bomb Survivors
Authors: Richardson D.B. , Wing S. , Cole S.R. .
Source: American Journal Of Epidemiology, 2013-03-15 00:00:00.0; 177(6), p. 562-8.
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Random Effects Regression Models For Trends In Standardised Mortality Ratios
Authors: Richardson D.B. , Cole S.R. , Chu H. .
Source: Occupational And Environmental Medicine, 2013 Feb; 70(2), p. 133-9.
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Regression Models For The Effects Of Exposure Rate And Cumulative Exposure
Authors: Richardson D.B. , Cole S.R. , Langholz B. .
Source: Epidemiology (cambridge, Mass.), 2012 Nov; 23(6), p. 892-9.
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Background Stratified Poisson Regression Analysis Of Cohort Data
Authors: Richardson D.B. , Langholz B. .
Source: Radiation And Environmental Biophysics, 2012 Mar; 51(1), p. 15-22.
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Model Averaging In The Analysis Of Leukemia Mortality Among Japanese A-bomb Survivors
Authors: Richardson D.B. , Cole S.R. .
Source: Radiation And Environmental Biophysics, 2012 Mar; 51(1), p. 93-5; discussion 97-100.
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Bayesian posterior distributions without Markov chains.
Authors: Cole S.R. , Chu H. , Greenland S. , Hamra G. , Richardson D.B. .
Source: American Journal Of Epidemiology, 2012-03-01 00:00:00.0; 175(5), p. 368-75.
EPub date: 2012-03-01 00:00:00.0.
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Lagging Exposure Information In Cumulative Exposure-response Analyses
Authors: Richardson D.B. , Cole S.R. , Chu H. , Langholz B. .
Source: American Journal Of Epidemiology, 2011-12-15 00:00:00.0; 174(12), p. 1416-22.
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A Comparison Of Methods To Estimate The Hazard Ratio Under Conditions Of Time-varying Confounding And Nonpositivity
Authors: Naimi A.I. , Cole S.R. , Westreich D.J. , Richardson D.B. .
Source: Epidemiology (cambridge, Mass.), 2011 Sep; 22(5), p. 718-23.
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Hierarchical Latency Models For Dose-time-response Associations
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Source: American Journal Of Epidemiology, 2011-03-15 00:00:00.0; 173(6), p. 695-702.
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Bias In The Estimation Of Exposure Effects With Individual- Or Group-based Exposure Assessment
Authors: Kim H.M. , Richardson D. , Loomis D. , Van Tongeren M. , Burstyn I. .
Source: Journal Of Exposure Science & Environmental Epidemiology, 2011 Mar-Apr; 21(2), p. 212-21.
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Flexible Modeling Of The Cumulative Effects Of Time-dependent Exposures On The Hazard
Authors: Hauptmann M. , Richardson D.B. .
Source: Statistics In Medicine, 2011-01-30 00:00:00.0; 30(2), p. 197; author reply 198-9.
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Discrete Time Hazards Models For Occupational And Environmental Cohort Analyses
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Latency Models For Analyses Of Protracted Exposures
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Source: Epidemiology (cambridge, Mass.), 2009 May; 20(3), p. 395-9.
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