Hierarchical regression for analyses of multiple outcomes

Details

• Personal Author:
  Chu H ; Cole SR ; Hamra GB ; MacLehose RF ; Richardson DB
• Description:
  In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical "shrinkage" approach to stabilize estimates of an exposure's associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. [Description provided by NIOSH]
• Subjects:
  Accident Prevention Accidents Accidents, Occupational Geology Mining Occupational Health Safety
• Keywords:
  Author Keywords: Poisson Regression Cancer Chemical-composition Chemical-properties Cohort Studies Demographic-characteristics Dioxins Epidemiologic Methods Exposure-levels Humans Men Model Models Morbidity-rates Mortality-rates Radiation-exposure Statistical Models Statistics Women

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• ISSN:
  0002-9262
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  North Carolina ; OSHA Region 4
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  459-467
• Volume:
  182
• Issue:
  5
• NIOSHTIC Number:
  nn:20047132
• Citation:
  Am J Epidemiol 2015 Sep; 182(5):459-467
• Contact Point Address:
  Dr. David B. Richardson, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
• Email:
  david.richardson@unc.edu
• Federal Fiscal Year:
  2015
• Performing Organization:
  University of North Carolina, Chapel Hill
• Peer Reviewed:
  True
• Start Date:
  20110901
• Source Full Name:
  American Journal of Epidemiology
• End Date:
  20140630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:9c7d9c2b4f77b1536c0b22c41f1a5edd8df00a54a84162f3c1aa4d72bd3a56091a78340497a2f2e67b6737445284179a2f1537ecb13aa195415d010c156bd820
• Download URL:
  https://stacks.cdc.gov/view/cdc/202315/cdc_202315_DS1.pdf
• File Type:
  [PDF - 263.36 KB ]