Key points are not available for this paper at this time.
Matrix algorithms are presented which generate estimates of t-year survival rates for patients with chronic disease from a categorical data approach. Weighted least squares has been applied to the resulting estimates to fit linear models which, together with large sample theory, provide a straightforward and unified method for testing hypotheses of interest. The sequential use of cross-product and hierarchical structures in a stepwise manner is described in detail as a useful descriptive approach to formulating efficient models. These results can be applied as a basis for "clustering" combinations of clinical findings into groups indicative of stage of disease.
Koch et al. (Fri,) studied this question.