Methods for informative censoring HIV/AIDS cohorts

描述(由申请人提供)：非信息性审查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。在审查是信息性的情况下，使用标准方法来描述生存数据，如Kaplan-Meier，对生存函数的估计是有偏差的。重新分发到正确的算法的扩展形式代表了一种可能处理信息性审查的新方法。这项研究将：1)扩展再分配到正确的算法方法，以估计存在信息的正确审查存在的生存函数；2)在信息正确的审查下，使用Bootstrap方法估计使用扩展算法估计的生存函数的标准误差；以及3)在Mac和活队列中，使用扩展算法评估在五种不同的审查场景下从HIV血清转换到死亡的时间分布。Mac和Alive代表了应用这种扩展方法的理想设置，因为在这些队列中执行的大部分事件间隔时间分析假设了非信息性审查。因此，对来自这些队列的数据应用扩展方法提供了完善这些研究中先前作出的推论的机会。

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.