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Accelerated failure time vs Cox proportional hazards mixture cure models: David vs Goliath?
Statistical Papers ( IF 1.3 ) Pub Date : 2022-07-29 , DOI: 10.1007/s00362-022-01345-5
Motahareh Parsa, Ingrid Van Keilegom

A mixture cure model relies on a model for the cure probability and a model for the survival function of the uncured subjects. For the latter, one often uses a Cox proportional hazards model. We show the identifiability of this model under weak assumptions. The model assumes that the cure threshold is the same for all values of the covariates, which might be unrealistic in certain situations. An alternative mixture cure model is the accelerated failure time (AFT) model. We also show the identifiability of this model under minimal assumptions. The cure threshold in this model depends on the covariates, which often leads to a better fit of the data. This is especially true when the follow-up period is insufficient for certain values of the covariates. We study these two models via simulations both when the follow-up is sufficient and when it is insufficient. Moreover, the two models are applied to data coming from a breast cancer clinical trial. We show that the AFT and the Cox model both fit the data well in the region of sufficient follow-up, but differ drastically outside that region.



中文翻译:

加速失效时间与 Cox 比例风险混合治疗模型:大卫与歌利亚?

混合治愈模型依赖于治愈概率模型和未治愈对象生存函数模型。对于后者,人们经常使用 Cox 比例风险模型。我们在弱假设下展示了该模型的可识别性。该模型假设所有协变量值的治愈阈值都相同,这在某些情况下可能是不现实的。另一种混合固化模型是加速失效时间 (AFT) 模型。我们还展示了该模型在最小假设下的可识别性。此模型中的治愈阈值取决于协变量,这通常会导致更好的数据拟合。当随访期对于协变量的某些值而言不足时尤其如此。我们通过模拟研究这两个模型,无论是在后续足够的情况下,还是在后续不足的情况下。此外,这两个模型适用于来自乳腺癌临床试验的数据。我们表明,AFT 和 Cox 模型在足够的后续区域中都很好地拟合了数据,但在该区域之外差异很大。

更新日期:2022-07-30
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