潜在影响因素与 Kaplan-Meier 估计量。
Hidden Imputations and the Kaplan-Meier Estimator.
出版信息
Am J Epidemiol. 2020 Nov 2;189(11):1408-1411. doi: 10.1093/aje/kwaa086.
Abstract
The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences.
摘要
生存函数的 Kaplan-Meier（KM）估计器为右删失和左截断观测值推断事件时间，但这些推断是隐藏的，因此有时不为应用健康科学家所识别。我们使用一个简单的示例数据集和再分配算法来说明 KM 估计器如何进行推断。我们还讨论了生存数据分析所需的假设。说明 KM 估计器隐藏的推断有助于澄清这些假设，从而可能减少不适当的推断。
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