Subscribe to RSS
DOI: 10.1055/s-0038-1634826
Multivariate Procedures to Describe Clinical Staging of Melanoma
Dr. Manton’s efforts in this research were supported by NIA Grant No.AG01159. Dr. Woodbury’s efforts were supported by NIA Grant No.AG03188. Dr. Wrigley’s efforts were supported, while he was a postdoctoral fellow at Duke University, by NIA Grant No. T32AG00139. Dr. Cohen’s efforts were supported by USPHS grant nos. CA14236 and CA36566.Publication History
Publication Date:
07 February 2018 (online)
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.
-
REFERENCES
- 1 Clive J, Woodbury MA, Siegler IC. Fuzzy and crisp set-theoretic-based classification of health and disease. J Med Systems 1983; 07: 317-32.
- 2 Woodbury MA, Manton KG. A new procedure for analysis of medical classification. Meth Inform Med 1982; 21: 210-20.
- 3 Manton KG, Woodbury MA, Stallard E. Statistical and measurement issues in assessing the welfare status of aged individuals and populations. J Econometrics. (in press).
- 4 Cohen HJ, Cox E, Manton KG, Woodbury MA. Malignant melanoma in the elderly. J Clin Oncol 1987; 05: 100-6.
- 5 Hartigan JA. Clustering Algorithms. New York: John Wiley & Sons; 1975
- 6 Terrell GR, Scott DW. Oversmoothed non-parametric density estimates. J Amer Statist Assoc 1985; 80: 209-14.
- 7 Woodbury MA, Clive J. Clinical pure types as a fuzzy partition. J Cybernetics 1974; 04: 111-21.
- 8 Woodbury MA, Clive J, Garson A. Mathematical typology: A grade of membership technique for obtaining disease definition. Comp Biomed Res 1978; 11: 277-98.
- 9 Tblley HD, Manton KG. Large sample properties of a fuzzy partition. J Statist Mathem (Tokyo). (in press).
- 10 Clark WH, From L, Bernardino E, Mihm M. The histogenesis and biologic behavior of primary human malignant melanomas of the skin. Cancer Research 1969; 29: 705-26.
- 11 Breslow A. Thickness, cross-sectional areas and depth of invasion in the prognosis of cutaneous melanoma. Ann Surgery 1987; 172: 902-8.
- 12 Singer BH. Grade of membership representations: concepts and problems. In: Anderson TW, Athreya KG, Inglehardt D. eds. Festschrift for Samuel Karlin. Orlando Fla: Academic Press; 1989: 317-34.
- 13 Thompson JS, Wekstein DR, Rhoades JS. et al. The immune status of healthy centena-j rians. J Amer Gerontol Soc 1984; 32: 274-81.
- 14 Wedelin C, Bjorkholm M, Holm G. et al. Blood T-lymphocyte functions in healthy adults in relation to age. Scand J Haematol 1982; 28: 45.
- 15 Kaesberg PR, Ershler WB. The change in tumor aggressiveness with age: lessons from experimental animals. Seminars in Oncology 1989; 16: 28-33.