Abstract
Analyzing multivariate clinical data to identify subclasses of patients being treated
for a specific disease may improve patient management and increase understanding of
the behavior of disease under clinical conditions. In some cases, patients have been
classified on prognostic characteristics using standard risk assessment procedures
(e.g.. Cox’ regression). This requires long term follow-up, differentiates patients
only on attributes relevant to survival, and assumes that patients are sampled from
a common population. Other approaches involve the use of clustering algorithms to
classify patients into categories based on multiple clinical attributes. We illustrate
the use of a multivariate statistical procedure to directly characterize patients
on multiple clinical characteristics. The procedure is designed to analyze discrete
response data with parameters representing individual differences within groups. Its
use is illustrated for patients with Stage I melanoma in determining how age is related
to treatment response in different patient groups.
Key-Words
Stage I Melanoma - Fuzzy Sets - Grade of Membership - Disease Staging - Aging
