Adjustment for Suspected Misclassified Smoking Data in an Historical Cohort Study of Workers Exposed to Acrylonitrile

Downing Zimmerman, Sarah (2014) Adjustment for Suspected Misclassified Smoking Data in an Historical Cohort Study of Workers Exposed to Acrylonitrile. Master's Thesis, University of Pittsburgh. (Unpublished)

Preview

PDF Primary Text Download (679kB) | Preview

Abstract

Objectives: To examine the association between exposure to acrylonitrile (AN) and lung cancer mortality after properly addressing misclassification and possible positive confounding of smoking history.

Methods: Subjects were 992 white males who were employed for three or more months between 1960 and 1996 at an AN chemical plant in Lima, OH. There were 15 identified cases of lung cancer deaths. Smoking histories were obtained for 90.3% of the cohort and 54.2% of the cohort were identified as having “ever smoked”. Though there were few “unknown” smoking histories, the smoking variable was determined to be misclassified as the RR for having ever smoked related to lung cancer was only 1.08 (95% CI=0.26, 6.18). We addressed potential confounding by smoking in the presence of suspected misclassified smoking data by determining if a reasonable adjustment of the available smoking data would change the risk levels of lung cancer in the original Lima cohort and the relationship between AN exposure and lung cancer using Monte Carlo simulation and bias adjustment.

Conclusions: After running Monte Carlo simulation, we found that the mean RR of lung cancer mortality given differing levels of AN exposure decreased after adjusting for the simulated smoking data. However, the results from the bias adjustment must be interpreted with caution as the analysis was limited by the number of lung cancer cases. In this cohort, we concluded that smoking positively confounded the relationship between AN exposure and lung cancer mortality.

Public Health Relevance: Properly adjusting for smoking history in studies of lung cancer is critical of the validity of the study results. As seen in this study, smoking habits impact the risk of certain health outcomes. Researchers must attempt to address the potential confounding by smoking whenever possible.

---

Share

Citation/Export:	Select format... Citation - Text Citation - HTML Endnote BibTex Dublin Core OpenURL MARC (ISO 2709) METS MODS EP3 XML Reference Manager Refer
Social Networking:	Share |

---

Details

Item Type:	University of Pittsburgh ETD
Status:	Unpublished
Creators/Authors:	Creators Email Pitt Username ORCID Downing Zimmerman, Sarah scd27@pitt.edu SCD27
ETD Committee:	Title Member Email Address Pitt Username ORCID Thesis Advisor Marsh, Gary gmarsh@pitt.edu GMARSH Committee Member Youk, Ada ayouk@pitt.edu AYOUK Committee Member Talbott, Evelyn O. eot1@pitt.edu EOT1 Committee Member Collins, James jjcollins@dow.com
Date:	29 January 2014
Date Type:	Publication
Defense Date:	13 November 2013
Approval Date:	29 January 2014
Submission Date:	10 December 2013
Access Restriction:	5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages:	54
Institution:	University of Pittsburgh
Schools and Programs:	School of Public Health > Biostatistics
Degree:	MS - Master of Science
Thesis Type:	Master's Thesis
Refereed:	Yes
Uncontrolled Keywords:	confounding by smoking, lung cancer mortality, relative risk regression, monte carlo simulation, bias adjustment
Date Deposited:	29 Jan 2014 17:12
Last Modified:	01 Jan 2019 06:15
URI:	http://d-scholarship.pitt.edu/id/eprint/20263

---

Metrics

Monthly Views for the past 3 years

Plum Analytics

---

Actions (login required)

View Item