Yu Menggang, Yiannoutsos Constantin T
Department of Biostatistics & Medical Informatics, University of Wisconsin - Madison.
Department of Biostatistics, Indiana University Purdue University Indianapolis.
Scand Stat Theory Appl. 2015 Mar 1;42(1):87-103. doi: 10.1111/sjos.12096.
Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach program to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi-competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study, and analysis of the study data.
信息性删失对于任何生物医学研究来说都是一个棘手的问题。大多数现有的统计方法试图通过对删失机制指定无法验证的假设来校正与此现象相关的估计偏差。我们考虑在非洲进行的一项队列研究，该研究使用一个外展项目来确定删失受试者的生命状态。这些数据可用于识别一些相关分布。然而，由于仅对一部分删失受试者进行了随访，生命状态的确定并不完整。我们使用半竞争风险方法作为我们的分析框架来处理终端事件未完全确定的这种特定情况，并考虑用于估计删失的边际分布以及生存的边际和条件分布的各种程序。我们还在我们的设定中考虑模型选择和估计效率。通过模拟、渐近研究以及对研究数据的分析来证明所提出方法的性能。
