Melissa Friesen, Ph.D.
Occupational and Environmental Epidemiology Branch
9609 Medical Center Drive
Rockville, MD 20850
Dr. Friesen's research has focused on quantitative assessment strategies to minimize exposure misclassification in occupational epidemiologic studies. She has focused on improving exposure estimates, evaluating the robustness of exposure-response relationships to exposure assessment strategies, and using statistical models for both developing exposure metrics and evaluating their exposure-response relationships. By using more refined and more proximal exposure measures, her research has resulted in quantitative exposure-response relationships for several exposure-disease associations that have not previously been published.
Decision Rule and Measurement-based Approaches
In the Occupational and Environmental Epidemiology Branch (OEEB), Dr. Friesen extends her research from industry-based studies to develop transparent measurement- and decision rule-based methods to assess occupational exposure in case-control studies and population-based cohorts. Exposure assessment in these studies relies heavily on subject-reported information and the professional judgment of exposure assessors. She has demonstrated that decision-rule based approaches are comparable to the estimates from a traditional job-by-job expert review and can replicate the average rating from a team of experts. She has also developed a method to use data mining methods (e.g. classification trees, CT) to extract underlying (but not explicitly stated) decision rules from the relationship between exposure estimates derived from professional judgment and questionnaire responses. The resulting CT decision trees extract the valuable information from previous assessments and thus allow the decision rules to be used to estimate exposure in other studies with similar exposure information. As a result, exposure assessors can focus their attention on the exposure scenarios identified by the CT model as being difficult to assess and on scenarios that were not covered by the decision rules. Most importantly, identifying the decision rules removes the assessment from its much criticized ‘black box’.
To anchor the intensity estimate in the decision rules to a concentration scale, Dr. Friesen has extended the use of statistical models that are commonly used in industry-based studies to predict historical exposure to population-based studies. For example, she has developed a framework to combine subjective ratings of exposure from job exposure matrices (JEMs) and exposure measurements to better discriminate between time and job differences in exposure levels in population-based studies. She has also demonstrated the utility of meta-regression models to identify temporal trends and occupation- and industry-specific differences in occupational lead exposure using summary statistics extracted from the published literature.
Automated Methods to Process Free-text Responses
Dr. Friesen also develops exposure assessment tools to facilitate the use of occupational risk factor data in epidemiologic studies by efficiently transforming the verbatim participants’ responses from occupational questionnaires into usable data. She led the development of an algorithm, SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiological Research), to automatically code job descriptions into standardized occupation classification (SOC) codes. She also developed a keyword-based approach to use the verbatim responses to systematically extract variables representing exposure scenarios that can be used in decision rules.
Gender Differences in Exposure
During the Environmental Exposures and Women's Health seminar series held on October 5, 2010, Dr. Friesen discussed the importance of examining cancer risk separately in men and women. Specifically, she focused on sex differences in the accuracy of exposure assessment tools for epidemiologic studies. View Dr. Friesen's presentation, “Women are not just small men”. In analyses pooling occupational responses from three OEEB studies, she found gender differences in the prevalence and frequency of work tasks. These differences would result in exposure misclassification if these work task differences are not considered and demonstrate the need for incorporating job modules to capture individual-level occupational information.
Dr. Friesen received a Ph.D. (2006) and M.Sc. (2001) in occupational hygiene from the University of British Columbia in Vancouver, Canada. She completed postdoctoral studies at Monash University in Melbourne, Australia, and at the University of California at Berkeley. She joined the NCI as a tenure-track investigator in OEEB in June 2009, and was awarded NIH scientific tenure and promoted to senior investigator in 2017.
Wheeler DC, Burstyn I, Vermeulen R, Yu K, Shortreed SM, Pronk A, Stewart PA, Colt JS, Baris D, Karagas MR, Schwenn M, Johnson A, Silverman DT, Friesen MC. Inside the black box: starting to uncover the underlying decision rules used in a one-by-one expert assessment of occupational exposure in case-control studies. Occup Environ Med. 2013;70(3):203-10.
Friesen MC, Wheeler DC, Vermeulen R, Locke SJ, Zaebst DD, Koutros S, Pronk A, Colt JS, Baris D, Karagas MR, Malats N, Schwenn M, Johnson A, Armenti KR, Rothman N, Stewart PA, Kogevinas M, Silverman DT. Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study. Ann Occup Hyg. 2016;60(4):467-78.
Friesen MC, Pronk A, Wheeler DC, Chen YC, Locke SJ, Zaebst DD, Schwenn M, Johnson A, Waddell R, Baris D, Colt JS, Silverman DT, Stewart PA, Katki HA. Comparison of algorithm-based estimates of occupational diesel exhaust exposure to those of multiple independent raters in a population-based case-control study. Ann Occup Hyg. 2013;57(4):470-81.
Friesen MC, Coble JB, Lu W, Shu XO, Ji BT, Xue S, Portengen L, Chow WH, Gao YT, Yang G, Rothman N, Vermeulen R. Combining a job-exposure matrix with exposure measurements to assess occupational exposure to benzene in a population cohort in shanghai, china. Ann Occup Hyg. 2012;56(1):80-91.
Russ DE, Ho KY, Colt JS, Armenti KR, Baris D, Chow WH, Davis F, Johnson A, Purdue MP, Karagas MR, Schwartz K, Schwenn M, Silverman DT, Johnson CA, Friesen MC. Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies. Occup Environ Med. 2016;73(6):417-24.
Related Scientific Focus Areas
This page was last updated on May 1st, 2017