Methodological Challenges in the Statistical Analysis of Epidemiology Studies: use of Average Exposure Metrics in Historical Cohort Designs

Thomas B. Starr1, *, Gary M. Marsh2
1 TBS Associates, 7500 Rainwater Road, Raleigh NC 27615-3700, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill NC, USA
2 Department of Biostatistics, Department of Epidemiology, Department of Clinical & Translational Science, and Center for Occupational Biostatistics & Epidemiology, University of Pittsburgh, Pittsburgh PA, USA

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Creative Commons License
© Starr and Marsh; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the TBS Associates, 7500 Rainwater Road, Raleigh NC 27615-3700, USA; Tel: 919-876-0203; E-mail:


An important methodological challenge in the analysis of historical occupational cohort data is choosing the most appropriate metric for the average exposure of the workers under study. We describe and illustrate the many issues associated with this challenge using a recent re-analysis by Kopylev [1] of lung cancer mortality in the National Cancer Institute (NCI) acrylonitrile cohort study. Kopylev proposed the routine use of both Average Exposure and Average Intensity when analyzing epidemiological cohort data. However, due to the methodological issues that arise with these metrics, we have concerns about the validity of his finding of a significant positive association between workers’ acrylonitrile exposure and increased lung cancer mortality in a subset of the NCI cohort. These include 1) the opportunity for substantial selection bias to have impacted the results; 2) the failure to account properly for latency; 3) the absence of a convincing biological rationale or other a priori justification for Kopylev’s preferred exposure metrics; 4) the absence of meaningful differences in Average Exposure- and Average Intensity- based risk estimates; 5) the lack of a logical basis for using either of these exposure metrics and 6) the conclusion that smoking was not a significant positive confounder, which is at odds with all other such findings for this cohort.

Keywords: Average exposure, Average intensity, Confounding by smoking, Cumulative exposure, Lung cancer mortality, Selection bias.