Methods for informative censoring HIV/AIDS cohorts

描述（由申请人提供）：无信息删失的假设在流行病学文献中广泛存在。在大多数队列研究中，这一假设是不可验证的。在删失是提供信息的情况下，使用标准方法来描述生存数据，如Kaplan-Meier，产生生存函数的有偏估计。一个扩展形式的再分发到右边的算法代表了一种新的方法，可以潜在地处理信息审查。这项研究将：1）扩展右重分布算法方法以估计存在信息右删失的生存函数; 2）在信息右删失下，使用Bootstrap方法来估计使用扩展算法估计的生存函数的标准误差;和3）在MACS和ALIVE群组中，使用扩展算法，在五种不同的删失情景下评估从HIV血清转化到死亡的时间分布。MACS和ALIVE代表了应用这种扩展方法的理想设置，因为在这些队列中进行的大部分至事件时间分析假设为无信息删失。因此，将扩展方法应用于这些队列的数据提供了完善这些研究中先前推断的机会。

DESCRIPTION (provided by applicant): The assumption of non-informative censoring is widespread in epidemiologic literature. In most cohort studies, this assumption is not verifiable. In the case where censoring is informative use of standard approaches to describe survival data such as Kaplan-Meier yield biased estimates of the survival function. An expanded form of the redistribute-to-the-right algorithm represents a new method that could potentially deal with informative censoring. This study will: 1) expand redistribute-to-the-right algorithm methods to estimate the survival function in the presence of informative, right censoring; 2) under informative, right censoring, use bootstrap methods to estimate the standard error of the survival function estimated using the expanded algorithm; and 3) in MACS and ALIVE cohorts, assess the distribution of time from HIV seroconversion to death under five distinct censoring scenarios using the expanded algorithm. MACS and ALIVE represent ideal settings to apply this expanded method given that the bulk of time-to-event analyses performed in these cohorts assume non-informative censoring. As such, application of the expanded approach to data from these cohorts offers the opportunity to refine prior inferences made in these studies.