Evaluating Non-Linearities in the Exposure Response Relationship Using Nonparametric Smoothing and Conditional Logistic Regression

Details

• Personal Author:
  Eisen EA ; Kriebel D ; Sullivan, Patricia A. ; Wegman, David H. ; Woskie SR
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Environmental Monitoring Epidemiology Occupational Health Safety
• Keywords:
  Dose-response Exposure-assessment Exposure-levels Exposure-limits Models Statistical-analysis
• ISSN:
  0002-9262
• Document Type:
  Text
• Genre:
  Proceedings ; Abstract
• Place as Subject:
  OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DRDS - Division of Respiratory Disease Studies
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  151
• Issue:
  11
• NIOSHTIC Number:
  nn:20025072
• Citation:
  Am J Epi 2000 Jun; 151(11)(Suppl):S44
• Federal Fiscal Year:
  2000
• Peer Reviewed:
  False
• Source Full Name:
  American Journal of Epidemiology, Abstracts of the 33rd Annual Meeting of the Society for Epidemiologic Research, Seattle, Washington, June 15-17, 2000
• Supplement:
  Suppl
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0d1025e6d68529700400e53b734505ee4fa0cc7bb09960f1bac71f4ca88d3d4badd595e13eb2120926487d05cfdf287b781f197c7bfdbea4b317039b4e39115d
• Download URL:
  https://stacks.cdc.gov/view/cdc/196575/cdc_196575_DS1.pdf
• File Type:
  [PDF - 321.52 KB ]