The problem of accounting for the grouping of continuous, bivariate data in regression analyses is considered. Reasons why grouping must be taken seriously are advanced, and a strategy for accounting for grouping is demonstrated. The specific model asserts that, in the absence of grouping, the data would be bivariate normal. This model is used to adjust estimates of parameters in a regression relating disease severity to a grouped exposure variable, using data on pneumoconiosis in English coal miners (Ashford, 1959, Biometrics 15, 573-581). The choice of computing methods is discussed and likelihood formulas are presented.
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics