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，这个假设是无法验证的。在审查是使用标准方法来描述生存数据（例如 Kaplan-Meier 产生的生存函数有偏差估计）的信息性使用的情况下。重新分配到右侧算法的扩展形式代表了一种可能处理信息审查的新方法。本研究将：1）扩展重新分配到右侧的算法方法，以在存在信息丰富的右侧审查的情况下估计生存函数； 2）在信息丰富的右删失下，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； 3) 在 MACS 和 ALIVE 队列中，使用扩展算法评估五种不同审查场景下从 HIV 血清转化到死亡的时间分布。鉴于在这些队列中进行的大部分事件时间分析都假设非信息性审查，MACS 和 ALIVE 代表了应用这种扩展方法的理想设置。因此，对这些群体的数据应用扩展方法提供了完善这些研究中先前推论的机会。

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.