Multivariate analysis of prognostic factors in metastatic breast cancer.

Abstract

Univariate and multivariate analyses were conducted on data collected from the records of 619 patients with metastatic breast cancer in whom an Adriamycin-containing chemotherapeutic regimen was used. Using a forward, stepwise logistic regression procedure, several models or equations in which a small number of pretreatment factors were incorporated were generated and the probability of response to therapy was accurately predicted. The predictive ability of these models was tested retrospectively in 546 of the 619 patients from whom the data were derived and prospectively in a new population of 200 patients with metastatic breast cancer also treated with a therapeutically equivalent Adriamycin combination. Using similar univariate techniques, pretreatment factors were correlated with the length of survival after therapy. The proportional hazard model of Cox was used to develop a regression model relating survival to pretreatment characteristics in much the same manner as that of the response model. The total population of the initial group of patients was divided according to four levels of hazard ratio, and survival distributions were compared. This model also was tested progressively and its predictive capability was confirmed. The prediction of individual outcome is a valuable capability in the comparison of clinical trials and the continuing evaluation of biologic changes in patients with metastatic carcinoma; such a method is described in this paper.