A two-stage hierarchical regression model for meta-analysis of epidemiologic nonlinear dose-response data

  • Authors:

  • Qin Liu;Nancy R. Cook;Anna Bergström;Chung-Cheng Hsieh

  • Affiliations:

  • Biostatistical Research Group, Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA;Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA;Institute of Environmental Medicine, Karolinska Institute, SE-171 77 Stockholm, Sweden;Department of Cancer Biology, University of Massachusetts, 364 Plantation Street, LRB# 427, Worcester, MA 01605, USA

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2009

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Abstract

To estimate a summarized dose-response relation across different exposure levels from epidemiologic data, meta-analysis often needs to take into account heterogeneity across studies beyond the variation associated with fixed effects. We extended a generalized-least-squares method and a multivariate maximum likelihood method to estimate the summarized nonlinear dose-response relation taking into account random effects. These methods are readily suited to fitting and testing models with covariates and curvilinear dose-response relations.