Ascertaining Prognosis for Breast Cancer in Node-Negative Patients with Innovative Survival Analysis

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Abstract
Clinical decisions to administer adjuvant systemic therapy to women with early breast cancer require knowledge about baseline prognosis, which is only assessable in the absence of such adjuvant treatment, which most patients currently do receive. The Cox model is the standard tool for assessing the effect of prognostic factors; however, there may be substantive differences in the estimated prognosis obtained by the Cox model rather than a log‐normal model. For more than 50 years, clinical breast cancer data for cohorts of patients have supported the choice of a log‐normal model. The prognostic impact of model type is examined here for a cohort of breast cancer patients, only 7% of whom received adjuvant systemic therapy. We quantitated prognosis utilizing Kaplan–Meier, Cox, and log‐normal survival analyses for 415 consecutive T1–T3, M0, histologically node‐negative patients who were operated on for primary breast cancer at Women's College Hospital between 1977 and 1986. Recurrence outside the breast for disease‐free interval (DFI) and breast cancer death for disease‐specific survival (DSS) were the events of interest. The patient follow‐up for these investigations was 96% complete: a median 8 years for those surviving. Factors used in these investigations were age, weight, tumor size, histology, tumor grade, nuclear grade, lymphovascular invasion, estrogen receptor (ER), progesterone receptor (PR), combined ER/PR receptor, overexpression of neu oncoprotein, DNA ploidy, S‐phase, and adjuvant therapy. In our study we found evidence against the Cox assumption of proportional hazards, which is not an assumption for the log‐normal approach. We identified patients with greater than 96% and others with less than 40% DSS at 10 years. The difference in prognosis determined by using the Cox versus the log‐normal model ranged for DFI from 1.2% to 8.1%, and for DSS from 0.4% to 6.2%; interestingly, the difference was more substantial for patients with a high risk of recurrence or death from breast cancer. Estimated prognoses may differ substantially by survival analysis model type, by amounts that might affect patient management, and we think that the log‐normal model has a major advantage over the Cox model for survival analysis.