PubMedDate not supplied
Certain prognostic factors (patient and/or tumour characteristics) may be associated with low (or high) risk for local recurrence. Patients with these characteristics could be candidates for less (or more) adjuvant therapy or a less (or more) aggressive surgical approach. However, the assessment of many factors can be problematic with the standard multivariate technique-a Cox proportional hazards model and step-wise regression. We compared the results obtained when using a Cox model with those from four alternative models (exponential, Weibull, log logistic and log Normal) in step-wise and all subset regressions. Between 1977 and 1986, 293 primary invasive breast cancer patients were treated at the Henrietta Banting Breast Centre with a lumpectomy with or without an axillary dissection, and with no postoperative adjuvant therapy. The variables considered were age, lymph node status, tumour size, estrogen receptor (ER), progesterone receptor (PgR), histologic grade, nuclear grade, carcinoma in situ (CIS), amount of CIS, and presence of tumour emboli. With follow-up to 1991, nodal status was not found to be included in the step-wise Cox model, although it was in the step-wise exponential, Weibull and log Normal models, and in the best all subset models for all model types. The variables tumour emboli, ER, age, CIS and nodal status were consistently included in the best all subset regressions, regardless of model type. In the 1993 follow-up, the variables in the step-wise Cox model were tumour emboli, ER, age, CIS and nodal status. The multivariate consideration of all possible subsets of regression variables led to an earlier indication of the importance of nodal status, while the data strongly supported accelerated failure time models over the Cox model.