一种用于对可能存在信息删失的事件发生时间数据进行敏感性分析的多重填补方法。
A multiple imputation method for sensitivity analyses of time-to-event data with possibly informative censoring.
作者信息
Zhao Yue, Herring Amy H, Zhou Haibo, Ali Mirza W, Koch Gary G
机构信息
a Duke Clinical Research Institute , Durham , North Carolina , USA.
出版信息
J Biopharm Stat. 2014;24(2):229-53. doi: 10.1080/10543406.2013.860769.
Abstract
This article presents a multiple imputation method for sensitivity analyses of time-to-event data with possibly informative censoring. The imputed time for censored values is drawn from the failure time distribution conditional on the time of follow-up discontinuation. A variety of specifications regarding the post-discontinuation tendency of having events can be incorporated in the imputation through a hazard ratio parameter for discontinuation versus continuation of follow-up. Multiple-imputed data sets are analyzed with the primary analysis method, and the results are then combined using the methods of Rubin. An illustrative example is provided.
摘要
本文提出了一种多重填补方法，用于对可能存在信息删失的事件发生时间数据进行敏感性分析。删失值的填补时间是根据随访终止时间从失效时间分布中抽取的。通过一个用于随访终止与继续的风险比参数，可以在填补过程中纳入关于事件发生的终止后趋势的各种设定。使用主要分析方法对多重填补数据集进行分析，然后使用鲁宾方法合并结果。文中给出了一个示例。
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