Chapman J W, Hanna W, Kahn H J, Lickley H L, Wall J, Fish E B, McCready D R
Henrietta Banting Breast Centre, Women's College Hospital and University of Toronto, Ontario, Canada.
Surg Oncol. 1996 Oct-Dec;5(5-6):265-71. doi: 10.1016/s0960-7404(96)80031-4.
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.
某些预后因素（患者和/或肿瘤特征）可能与局部复发的低（或高）风险相关。具有这些特征的患者可能适合接受较少（或较多）的辅助治疗或采用较不（或更）积极的手术方法。然而，使用标准多变量技术——Cox比例风险模型和逐步回归来评估许多因素可能存在问题。我们比较了在逐步回归和所有子集回归中使用Cox模型与四种替代模型（指数模型、威布尔模型、对数逻辑模型和对数正态模型）所得的结果。1977年至1986年期间，293例原发性浸润性乳腺癌患者在亨丽埃塔·班廷乳腺中心接受了保乳手术，伴或不伴腋窝清扫，且未接受术后辅助治疗。所考虑的变量包括年龄、淋巴结状态、肿瘤大小、雌激素受体（ER）、孕激素受体（PgR）、组织学分级、核分级、原位癌（CIS）、CIS数量以及肿瘤栓子的存在情况。随访至1991年时，发现逐步Cox模型未纳入淋巴结状态，尽管它在逐步指数模型、威布尔模型和对数正态模型以及所有模型类型的最佳所有子集模型中。无论模型类型如何，变量肿瘤栓子、ER、年龄、CIS和淋巴结状态始终包含在最佳所有子集回归中。在1993年的随访中，逐步Cox模型中的变量为肿瘤栓子、ER、年龄、CIS和淋巴结状态。对回归变量的所有可能子集进行多变量考量可更早地表明淋巴结状态的重要性，同时数据强烈支持加速失效时间模型优于Cox模型。
