Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
Bone Marrow Transplant. 2022 Jun;57(6):853-856. doi: 10.1038/s41409-022-01627-4. Epub 2022 Mar 24.
One of the most important endpoints in haematopoietic cell transplant research is survival. A common objective is to interrogate which, if any, co-variates correlate with these endpoints. The most common statistical approach uses the Cox proportional hazards model. However, there are several problems and limitations of using this model including assumptions of proportional hazards and homogenous effects. In contrast, results of transplant studies often show non-proportional hazards because of early transplant-related mortality such that there is a survival disadvantage to transplants early on followed by a benefit. Even when a transplant proves better than a comparator not all transplant recipients benefit equally and some may be disadvantaged. Also, the favourable or unfavourable impact of a co-variate may vary in different time intervals. The accelerated failure time model which directly evaluates the association between survival and co-variates has similar limitations. Also, these models confer only a static view of the treatment effect. Several articles in our statistics series such as that by Zhen-Huan Hu and us (Bone Marrow Transplant. 2021 Aug 19. doi: 10.1038/s41409-021-01435-2), by Zhen-Huan Hu, Hai-Lin Wang and us and forthcoming articles by Megan Othus and by Liesbeth C. de Wreede, Johannes Schetelig and Hein Putter discuss issues in proper analyses of survival data from transplant studies including observational databases and randomized controlled trials. Are there better alternatives? A new popular model is quantile regression. In this typescript Bo Wei concisely introduce the quantile regression model for right censored data. He uses data from a Center for International Blood and Marrow Transplant Research (CIBMTR) registry study to show how to use the quantile regression and interpret the results. He also discusses use of quantile regression in complex survival analyses such as competing risk data or non-compliant data. Quantile regression is a natural, powerful approach for analyzing censored data with heterogenous co-variate effects. It has advantages compared with other survival models in depicting the dynamic association between survival outcome and co-variates. It can be applied to other transplant outcomes such as cumulative incidence of relapse, event-free and relapse-free survivals. There is an equation, but only one. Remember: The only thing to fear is fear itself (FDR). Please stick with it and you will be rewarded.Robert Peter Gale MD, PhD, DSc(hc), FACP, FRCP, FRCPI(hon), LHD, DPS, Mei-Jie Zhang PhD.
造血细胞移植研究中最重要的终点之一是生存。一个常见的目标是探讨哪些协变量（如果有的话）与这些终点相关。最常用的统计方法是使用 Cox 比例风险模型。然而，使用该模型存在一些问题和局限性，包括比例风险和同质效应的假设。相比之下，移植研究的结果通常显示出非比例风险，因为早期与移植相关的死亡率较高，导致早期移植存在生存劣势，随后带来益处。即使移植证明优于对照，并非所有移植受者都能同等受益，有些受者可能处于劣势。此外，协变量的有利或不利影响在不同的时间间隔可能不同。直接评估生存与协变量之间关联的加速失效时间模型也存在类似的局限性。此外，这些模型仅提供治疗效果的静态视图。我们统计学系列中的几篇文章，如 Zhen-Huan Hu 和我们的文章（Bone Marrow Transplant. 2021 Aug 19. doi: 10.1038/s41409-021-01435-2）、Zhen-Huan Hu、Hai-Lin Wang 和我们的以及 Megan Othus 和 Liesbeth C. de Wreede、Johannes Schetelig 和 Hein Putter 的即将发表的文章，讨论了移植研究中生存数据的适当分析问题，包括观察性数据库和随机对照试验。是否有更好的选择？一种新的流行模型是分位数回归。在这份打字稿中，Bo Wei 简明地介绍了右删失数据的分位数回归模型。他使用来自国际血液和骨髓移植研究中心（CIBMTR）注册研究的数据，展示了如何使用分位数回归并解释结果。他还讨论了分位数回归在复杂生存分析中的应用，如竞争风险数据或不符合数据。分位数回归是一种分析具有异质协变量效应的删失数据的自然、强大的方法。与其他生存模型相比，它在描述生存结果与协变量之间的动态关联方面具有优势。它可应用于其他移植结果，如累积复发率、无事件和无复发生存率。有一个方程式，但只有一个。记住：唯一需要害怕的就是恐惧本身（FDR）。请坚持下去，你会得到回报的。Robert Peter Gale MD, PhD, DSc(hc), FACP, FRCP, FRCPI(hon), LHD, DPS, Mei-Jie Zhang PhD.
