Chapman J A, Trudeau M E, Pritchard K I, Sawka C A, Mobbs B G, Hanna W M, Kahn H, McCready D R, Lickley L A
Henrietta Banting Breast Centre, Women's College Hospital, University of Toronto, Ontario, Canada.
Breast Cancer Res Treat. 1992;22(3):263-72. doi: 10.1007/BF01840839.
Clinical studies usually employ Cox step-wise regression for multivariate investigations of prognostic factors. However, commercial packages now allow the consideration of accelerated failure time models (exponential, Weibull, log logistic, and log normal), if the underlying Cox assumption of proportional hazards is inappropriate. All-subset regressions are feasible for all these models. We studied a group of 378 node positive primary breast cancer patients accrued at the Henrietta Banting Breast Centre of Women's College Hospital, University of Toronto, between January 1, 1977, and December 31, 1986. 85% of these patients had complete prognostic factor data for multivariate analysis, and 96% of the patients were followed to 1990. There was evidence of marked departures from the proportional hazards assumption with two prognostic factors, number of positive nodes and adjuvant systemic therapy. The data strongly supported the log normal model. The all-subset regressions indicated that three models were similarly good. The variables 1) number of positive nodes, 2) tumour size, and 3) adjuvant systemic therapy were included in all three models along with one of three biochemical receptor variables 1) ER, 2) combined receptor (ER- PgR-; ER+PgR-; ER- PgR+; ER+PgR+; or 3) PgR. Better multivariate modeling was achieved by using quantitative prognostic factors, a check for appropriate underlying model-type, and all-subset variable selection. All-subset regressions should be considered for routine use with the many new prognostic factors currently under evaluation; it is very possible that there may not be a single model that is substantially better than others with the same number of variables.
临床研究通常采用Cox逐步回归对预后因素进行多变量研究。然而，如果潜在的Cox比例风险假设不适用，现在的商业软件包允许考虑加速失效时间模型（指数模型、威布尔模型、对数逻辑模型和对数正态模型）。所有子集回归对所有这些模型都是可行的。我们研究了一组378例淋巴结阳性的原发性乳腺癌患者，这些患者于1977年1月1日至1986年12月31日在多伦多大学女子学院医院的亨丽埃塔·班廷乳腺中心入组。其中85%的患者拥有完整的预后因素数据用于多变量分析，96%的患者随访至1990年。有证据表明，两个预后因素，即阳性淋巴结数量和辅助全身治疗，明显偏离了比例风险假设。数据强烈支持对数正态模型。所有子集回归表明，有三个模型同样良好。所有三个模型都纳入了以下变量：1）阳性淋巴结数量、2）肿瘤大小和3）辅助全身治疗，以及三个生化受体变量之一：1）雌激素受体（ER）、2）联合受体（ER -孕激素受体（PgR）-；ER +PgR -；ER -PgR +；ER +PgR +）或3）PgR。通过使用定量预后因素、检查合适的潜在模型类型以及所有子集变量选择，可以实现更好的多变量建模。对于目前正在评估的许多新预后因素，应考虑将所有子集回归用于常规分析；很有可能不存在一个在变量数量相同的情况下明显优于其他模型的单一模型。
