Power for time-to-event analyses is usually assessed under continuous-time models. Often, however, times are discrete or grouped, as when the event is only observed when a procedure is performed. Wallenstein and Wittes (Biometrics, 1993) describe the power of the Mantel-Haenszel test for discrete lifetables under their chained binomial model for specified vectors of event probabilities over intervals of time. Herein, the expressions for these probabilities are derived under a piecewise exponential model allowing for staggered entry and losses to follow-up. Radhakrishna (Biometrics, 1965) showed that the Mantel-Haenszel test is maximally efficient under the alternative of a constant odds ratio and derived the optimal weighted test under other alternatives. Lachin (Biostatistical Methods: The Assessment of Relative Risks, 2011) described the power function of this family of weighted Mantel-Haenszel tests. Prentice and Gloeckler (Biometrics, 1978) described a generalization of the proportional hazards model for grouped time data and the corresponding maximally efficient score test. Their test is also shown to be a weighted Mantel-Haenszel test, and its power function is likewise obtained. There is trivial loss in power under the discrete chained binomial model relative to the continuous-time case provided that there is a modest number of periodic evaluations. Relative to the case of homogeneity of odds ratios, there can be substantial loss in power when there is substantial heterogeneity of odds ratios, especially when heterogeneity occurs early in a study when most subjects are at risk, but little loss in power when there is heterogeneity late in a study.

译文

事件时间分析的能力通常在连续时间模型下进行评估。但是,时间通常是离散的或分组的,例如仅在执行过程时才观察到事件。Wallenstein和Wittes (生物计量学,1993) 描述了Mantel-Haenszel测试在其链式二项式模型下离散生命表的功能,该模型针对指定的事件概率向量在时间间隔内。在此,这些概率的表达式是在分段指数模型下得出的,该模型允许交错进入和损失进行跟踪。Radhakrishna (Biometrics,1965) 表明,Mantel-Haenszel测试在恒定比值比的替代方案下是最大效率的,并在其他替代方案下得出了最佳加权测试。Lachin (生物统计学方法: 相对风险评估,2011) 描述了该家族加权Mantel-Haenszel检验的幂函数。Prentice和Gloeckler (Biometrics,1978) 描述了分组时间数据的比例风险模型的推广以及相应的最大有效分数测试。他们的检验也被证明是加权的Mantel-Haenszel检验,并且同样获得了其幂函数。与连续时间情况相比,离散链式二项式模型下的功率损失很小,前提是定期评估数量适中。相对于优势比同质性的情况,当优势比存在实质性异质性时,功率可能会出现实质性损失,尤其是当大多数受试者处于危险中的研究早期出现异质性时,但当研究后期存在异质性时,功率损失很小。

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