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doi: 10.1097/EDE.0000000000000045

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

Naimi, Ashley I.a; Cole, Stephen R.a; Hudgens, Michael G.b; Richardson, David B.a

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In the article by Naimi et al. that appeared on page 248 of the March 2014 issue of Epidemiology, “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,” the interpretation of the structural nested accelerated failure time model parameter was incomplete and potentially misleading. A correct interpretation is that exp(−ψ) corresponds to the effect (on the survival time ratio scale) of a 100 fiber-year/ml increase in cumulative asbestos in the last year of a worker’s employment at the facility under study, where the worker remains completely unexposed thereafter. This has been referred to as a “blip function” interpretation in the causal inference literature. The authors are grateful to Sally Picciotto for pointing this out.

Epidemiology. 26(5):778, September 2015.

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Background: Previous estimates of the effect of occupational asbestos on lung cancer mortality have been obtained by using methods that are subject to the healthy-worker survivor bias. G-estimation of a structural nested model provides consistent exposure effect estimates under this bias.

Methods: We estimated the effect of cumulative asbestos exposure on lung cancer mortality in a cohort comprising 2564 textile factory workers who were followed from January 1940 to December 2001.

Results: At entry, median age was 23 years, with 42% of the cohort being women and 20% nonwhite. During the follow-up period, 15% of person-years were classified as occurring while employed and 13% as occupationally exposed to asbestos. For a 100 fiber-year/ml increase in cumulative asbestos, a Weibull model adjusting for sex, race, birth year, baseline exposure, and age at study entry yielded a survival time ratio of 0.88 (95% confidence interval = 0.83 to 0.93). Further adjustment for work status yielded no practical change. The corresponding survival time ratio obtained using g-estimation of a structural nested model was 0.57 (0.33 to 0.96).

Conclusions: Accounting for the healthy-worker survivor bias resulted in a 35% stronger effect estimate. However, this estimate was considerably less precise. When healthy-worker survivor bias is suspected, methods that account for it should be used.

© 2014 by Lippincott Williams & Wilkins, Inc

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