title: Random effects regression models for trends in standardised mortality ratios.
abstract: OBJECTIVE
  Standardised mortality ratios (SMRs) play an important role in the epidemiological literature, particularly in evaluations of occupational hazards. While some authors have argued that comparisons of SMRs should be avoided, many investigators find such analyses appealing particularly when data are sparse. For example, calendar period-specific SMRs often are examined to identify emerging problems or to assess whether a hazard that impacted death rates in the past has abated. However, because the distribution of people with respect to age usually changes as calendar time advances, comparisons of SMRs across calendar periods can produce misleading results.
  
  
  METHODS
  We propose a random effects model to reduce the potential bias arising from comparisons of SMRs. This approach is illustrated using data from a study of workers employed at the Department of Energy's Oak Ridge National Laboratory.
  
  
  RESULTS
  When there is homogeneity across strata of covariates in the ratio of death rates in the target population to that in the reference population, the proposed model yields results equivalent to those obtained by a classical analysis of SMRs. However, as evidence against such homogeneity increases, the model yields a random effects version of SMRs for which patterns will conform better to those obtained from an internal analysis of rate ratios.
  
  
  CONCLUSIONS
  The proposed random effects model can reduce potential bias arising in the comparisons of SMRs.
authors.affiliation: Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. david_richardson@unc.edu
authors.forename: David B
authors.initials: DB
authors.lastname: Richardson
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
authors.forename: Haitao
authors.initials: H
authors.lastname: Chu
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D002423:Cause of Death; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D009784:Occupational Diseases / Q000401:mortality*; D012015:Reference Standards; D012044:Regression Analysis; D015983:Selection Bias; D055815:Young Adult