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Evaluating non-linearities in the exposure response relationship using nonparametric smoothing and conditional logistic regression.

Sullivan-PA; Eisen-EA; Kriebel-D; Woskie-SR; Wegman-DH
Am J Epi 2000 Jun; 151(11)(Suppl):S44
This paper applies nonparametric smoothing techniques in exploratory epidemiologic analysis to help describe exposure-response relationships Typically, dose-response models assume that the relation between exposure and response is linear on some scale. Many disease mechanisms, however, such as sensitization or carcinogenesis, may produce non-linearities in the dose-response curve. Moreover, linear models may be inappropriate in occupational epidemiology studies where the healthy worker effect can lead to an apparent plateau or even down-turn in risk among the more highly exposed. Occupational epidemiologists typically resort to categorical exposure variables to avoid linearity assumptions, but results are not robust to changes in cut-points. Nonparametric graphing methods make no a priori assumption about the shape of the exposure-response curve and so can identify empirical cut-points between homogeneous exposure categories As illustrated using data from a study of stomach cancer risk among auto workers exposed to metalworking fluids, exposure categories based on empirically identified cut-points were evaluated in conditional logistic regression models that controlled for confounding. Model fit was better and the risk estimates higher than in models based on traditional cut-points (selected a priori). For example, initial categorical analysis based on quar. tiles of the exposure distribution found an odds ratio of 1.4 (95% CIO.8. 2.5) in the highest category of exposure (> 1.9 mg/m'). Empirical cut-points identified after smoothing resulted in a model with better fit, a higher cut- off for the highest exposure category, and an odds ratio of 1.9 (95% CI 1.0- 3.6) among those exposed to at least 4 mg/m'. These methods have potential widespread application in epidemiologic analysis.
Epidemiology; Exposure-assessment; Exposure-levels; Exposure-limits; Dose-response; Models; Statistical-analysis
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Conference/Symposia Proceedings; Abstract
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NIOSH Division
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American Journal of Epidemiology, Abstracts of the 33rd Annual Meeting of the Society for Epidemiologic Research, Seattle, Washington, June 15-17, 2000