Pan W
Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building (Box 303), Minneapolis, MN 55455-0378, USA.
Stat Med. 2000 Jan 15;19(1):1-11. doi: 10.1002/(sici)1097-0258(20000115)19:1<1::aid-sim296>3.0.co;2-q.
Interval censored data arise naturally in large scale panel studies where subjects can only be followed periodically and the event of interest can only be recorded as having occurred between two examination times. In this paper we consider the problem of comparing two interval-censored samples. We propose to impute exact failure times from interval-censored observations to obtain right censored data, then apply existing techniques, such as Harrington and Fleming's G(rho) tests to imputed right censored data. To appropriately account for variability, a multiple imputation algorithm based on the approximate Bayesian bootstrap (ABB) is discussed. Through simulation studies we find that it performs well. The advantage of our proposal is its simplicity to implement and adaptability to incorporate many existing two-sample comparison techniques for right censored data. The method is illustrated by reanalysing the Breast Cosmesis Study data set.
区间删失数据在大规模面板研究中自然出现，在这类研究中，只能定期跟踪受试者，且感兴趣的事件只能记录为发生在两次检查时间之间。在本文中，我们考虑比较两个区间删失样本的问题。我们建议从区间删失观测值中推算出确切的失效时间，以获得右删失数据，然后将现有技术，如哈林顿和弗莱明的G(rho)检验应用于推算出的右删失数据。为了适当地考虑变异性，讨论了一种基于近似贝叶斯自助法(ABB)的多重插补算法。通过模拟研究，我们发现它表现良好。我们提议的优点是实施简单，并且能够纳入许多现有的右删失数据两样本比较技术。通过重新分析乳房美容研究数据集来说明该方法。
