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Besides the information regarding his/her disease, each hospitalized cancer patient also provides the variety of data regarding his/her psychological, cultural, social, economical, genetic, constitutional and medical background. The aim of this study was to introduce a holistic approach to analysis of medical data, in this case clinical data regarding cancer of the corpus uteri. Such approach requires the collection of data regarding different aspects of the cancer patient, and after the satisfactory sample size is obtained (which should be at least five times greater than the number of examined patient characteristics), the performance of factor analysis. In this study, the authors have processed the data regarding 25 characteristics of 928 corpus uteri cancer patients treated between 1980 and 1990 at the Department for Gynecological Oncology of the University Hospital for Gynecology and Obstetrics, Zagreb, Croatia. In factor analysis, the principal components were rotated after the initial extraction (the authors recommended the use of oblimin rotation) in order to obtain better ground for interpretation of the obtained results. The next step in this approach was the stepwise exclusion of characteristics with smallest communalities according to Kaiser-Meyer-Olkin criteria, and retaining the characteristics and components with the most significant impact on the explained system variance. When the number of principal components and initial analyzed characteristics was reduced to 3-4 and 7-10, respectively, the ultimate interpretations and conclusions were made. This approach outlined some clusters of correlations between medical data which are difficult to identify using other statistical procedures, primarily the impacts of various socioeconomic and hereditary-constitutional variables on overall survival. |
