Hu Zhen-Huan, Peter Gale Robert, Zhang Mei-Jie
Center for International Blood & Marrow Transplant Research, Medical College of Wisconsin, Milwaukee, WI, USA.
Centre for Haematology Research, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK.
Bone Marrow Transplant. 2020 Mar;55(3):538-543. doi: 10.1038/s41409-019-0552-y. Epub 2019 May 17.
Large randomized clinical trials testing the impact of subject-, disease- and transplant-related co-variates on outcomes amongst recipients of haematopoietic cell transplants are uncommon. For example, who is the best donor, which is the best pretransplant conditioning regimen or the best regimen to prevent or treat acute and/or chronic graft-versus-host disease. To answer these questions we often rely on analyses of data from large observational datasets such as those of the Center for International Blood and Marrow Transplant Research (CIBMTR) and the European Society for Blood and Marrow Transplantation (EBMT). Such analyses have proved extremely important in advancing the field. However, in contrast to randomized trials, we cannot be certain potentially important prognostic or predictive co-variates are balanced between cohorts selected for comparison from an observational dataset, a limitation which can lead to incorrect conclusions. In the typescript which follows the authours describe a method to adjust for known imbalances in co-variates and get a closer approximation of the truth. They give two examples, the impact of a new pretransplant conditioning regimen on disease-free survival (DFS) in subjects with Ewing sarcoma and the impact of donor-type on treatment-related mortality (TRM) and leukaemia relapse in subjects with acute leukaemia. Direct adjusted survival and cumulative incidence function (CIF) analyses are an important step forward. These analyses can be done using available statistical packages and we encourage readers to use them rather than reporting unadjusted analyses. Finally, we must emphasize direct adjustment can only be done for know prognostic or predictive co-variates, not unknown co-variates. Unknown co-variates will be balanced in randomized trials which is why we do them. So direct adjustment is an important step forward but not a perfect substitute for randomized trials. But any step forward is important. To quote Laozi: (A journey of a thousand miles begins with a single step).
针对造血细胞移植受者中受者、疾病及移植相关协变量对预后影响的大型随机临床试验并不常见。例如，谁是最佳供者，哪种是最佳移植前预处理方案，或者预防或治疗急性和/或慢性移植物抗宿主病的最佳方案。为回答这些问题，我们常常依赖于对大型观察性数据集（如国际血液和骨髓移植研究中心（CIBMTR）及欧洲血液和骨髓移植学会（EBMT）的数据集）的数据进行分析。此类分析已证明在推动该领域发展方面极为重要。然而，与随机试验不同，我们无法确定从观察性数据集中选择用于比较的队列之间潜在重要的预后或预测协变量是否平衡，这一局限性可能导致错误结论。在接下来的文稿中，作者描述了一种方法，用于调整协变量中已知的不平衡情况，并更接近真实情况。他们给出了两个例子，一种新的移植前预处理方案对尤因肉瘤患者无病生存期（DFS）的影响，以及供者类型对急性白血病患者治疗相关死亡率（TRM）和白血病复发的影响。直接调整生存分析和累积发病率函数（CIF）分析是向前迈出的重要一步。这些分析可以使用现有的统计软件包来完成，我们鼓励读者使用它们，而不是报告未调整的分析结果。最后，我们必须强调，直接调整只能针对已知的预后或预测协变量进行，而不能针对未知协变量。未知协变量在随机试验中会达到平衡，这就是我们进行随机试验的原因。所以直接调整是重要的一步，但并非随机试验的完美替代。但任何向前的一步都很重要。引用老子的话：（千里之行，始于足下）
