Statistical Methodology Development in Blood Transfusion Protocol Research

描述(申请人提供)：在美国，伤害是导致多产寿命损失的主要原因，它消耗了所有捐献的红细胞(RBC)输血的10%-15%。在一级创伤中心，25%的患者接受至少一个单位的红细胞，而那些接受大量输血(MT)的患者，即在24小时内接受10个或更多单位的红细胞，消耗了所有红细胞输注的71%，住院死亡风险为40%。血浆和血小板(分开的成分)分别用于90%和71%的MT患者。尽管最近的临床和翻译研究水平很高，但在知识和障碍方面仍然存在重大差距。当务之急包括：1)对需要MT的患者进行更准确的预测，以及2)早期采用最佳的MT方案(即足够的血浆、血小板和RBC单位的体积和比例)是否可以改善患者的预后。创伤输血实践中的这些悬而未决的问题仍然存在，很大程度上是因为研究设计(回顾)和统计分析方法(标准回归建模)的限制，这些方法不太适合数据的高度动态性质。根据MT的标准定义对患者进行分组，通过排除那些确实需要MT方案，但在接受第10个RBC单位之前因手术或其他干预而死亡或止血的出血患者，引入了生存偏见。生存偏差也威胁到以前的研究，因为24小时累积输血率的标准使用和死亡率的回归建模不能确定治疗是否延长了生存期，或者患者必须存活足够长的时间才能接受治疗(例如，达到高血浆：血小板：红细胞比率)。使用替代统计策略，如依赖时间的比例风险回归，可能无法克服这些问题，因为可能会出现信息性审查和时间依赖的混淆。我们的目标是通过开发相关的潜在类别分析和经常性事件数据分析方法来解决这些问题。具体目标有两个：1)开发和评估潜在分类模型，以准确识别真正需要MT的出血患者，并取代现有的MT定义，作为评估预测算法性能的金标准。此外，新的黄金标准将帮助我们通过添加新的候选预测因子来增强现有预测算法的性能；以及2)开发一个多类型的复发事件模型，用于估计与时间相关的红细胞、血浆和血小板输血率，并评估它们对患者生存的影响。开发的方法将通过模拟研究进行广泛的测试，并随后使用前瞻性观察性多中心重大创伤输注(PROMMTT)研究的数据进行验证。这项研究的结果将指导未来比较有效性研究的设计和进行，并促进更快地将MT方案的创新改进从试验台转换到床边。我们的新统计方法有望在许多不同的临床环境和动态数据集上得到广泛应用。

DESCRIPTION (provided by applicant): In the U.S., injury is the leading cause of productive years of life lost and consumes 10-15% of all donated red blood cell (RBC) transfusions. While 25% of patients admitted to level 1 trauma centers receive at least one unit of RBCs, those receiving massive transfusion (MT), defined as 10 or more units within 24 hours, consume 71% of all RBC transfusions with a 40% risk of in-hospital mortality. Plasma and platelets (separate components) are given to 90% and 71% of MT patients, respectively. Despite high levels of recent clinical and translational research, significant gaps in knowledge and barriers remain. The most urgent include whether 1) more accurate prediction of the patients in need of MT, and 2) earlier intervention with the optimum MT protocol (i.e., sufficient volumes and ratios of plasma, platelet and RBC units) can improve patient outcomes. These unresolved issues in trauma transfusion practice persist largely because of constraints in study design (retrospective) and statistical analysis methods (standard regression modeling) that are poorly suited to the highly dynamic nature of the data. Subgrouping patients according to the standard definition of MT introduces survival bias by excluding the hemorrhaging patients who truly needed an MT protocol, but died or achieved hemostasis due to surgical or other intervention before receiving the 10th RBC unit. Survival bias also threatens previous studies because the standard use of cumulative 24 hour transfusion ratios and regression modeling of mortality cannot resolve whether the treatment prolonged survival or patients had to survive long enough to receive treatment (e.g., to achieve high plasma:platelet:RBC ratios). The use of alternate statistical strategies like time-dependent proporational hazards regression may not overcome these problems because of the potential for informative censoring and time-dependent confounding. Our objective is to address these issues by developing relevant methodology for latent class analysis and recurrent event data analysis. Two specific aims will be undertaken: 1) to develop and evaluate a latent class model to accurately identify the hemorrhaging patients who truly needed an MT and replace the existing MT definition as the gold standard in assessing the performance of prediction algorithms. Furthermore, the new gold standard will help us enhance the performance of existing predictive algorithms with the addition of new candidate predictors; and 2) to develop a multi-type recurrent event model for estimating time-dependent RBC, plasma, and platelet transfusion rates, and evaluating their impact on patient survival. The developed methods will be extensively tested by simulation studies and thereafter validated with data from the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study. Results from this research will guide the design and conduct of future comparative effectiveness research and facilitate more rapid translation of innovative improvements in MT protocols from bench to bedside. Our new statistical methods are expected to have broad application across many different clinical contexts and dynamic data sets.