Aalen Odd O, Cook Richard J, Røysland Kjetil
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
Lifetime Data Anal. 2015 Oct;21(4):579-93. doi: 10.1007/s10985-015-9335-y. Epub 2015 Jun 24.
Statistical methods for survival analysis play a central role in the assessment of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and many other fields. The most common approach to analysis involves fitting a Cox regression model including a treatment indicator, and basing inference on the large sample properties of the regression coefficient estimator. Despite the fact that treatment assignment is randomized, the hazard ratio is not a quantity which admits a causal interpretation in the case of unmodelled heterogeneity. This problem arises because the risk sets beyond the first event time are comprised of the subset of individuals who have not previously failed. The balance in the distribution of potential confounders between treatment arms is lost by this implicit conditioning, whether or not censoring is present. Thus while the Cox model may be used as a basis for valid tests of the null hypotheses of no treatment effect if robust variance estimates are used, modeling frameworks more compatible with causal reasoning may be preferrable in general for estimation.
生存分析的统计方法在评估心血管疾病、癌症及许多其他领域的随机临床试验中的治疗效果方面发挥着核心作用。最常见的分析方法是拟合一个包含治疗指标的Cox回归模型，并基于回归系数估计量的大样本性质进行推断。尽管治疗分配是随机的，但在未建模的异质性情况下，风险比并不是一个可以进行因果解释的量。出现这个问题的原因是，第一个事件时间之后的风险集由之前未失败的个体子集组成。无论是否存在删失，这种隐含的条件作用都会导致治疗组之间潜在混杂因素分布的平衡丧失。因此，虽然如果使用稳健的方差估计，Cox模型可以用作对无治疗效果零假设进行有效检验的基础，但一般来说，在估计方面，与因果推理更兼容的建模框架可能更可取。
