Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

中文翻译：

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创伤性脑损伤临床研究中处理缺失数据的统计指南

即使在最精心设计的创伤性脑损伤 (TBI) 临床研究中，数据缺失也是一个持续存在且不可避免的问题。丢失数据模式可能是由于参与者退出、不合规、技术问题甚至死亡造成的。本综述描述了 TBI 研究中常见的缺失数据类型，并评估了用于从这些数据中得出结论和做出临床决策的统计方法的优缺点。我们回顾了缺失值分析 (MVA) 的最新创新，这是一个相对较新的统计学分支，应用于临床 TBI 数据。我们的讨论重点是国际创伤性脑损伤研究 (InTBIR) 倡议项目的研究：转变 TBI 的研究和临床知识 (TRACK-TBI)，欧洲重症监护医学中急性 TBI 的合作研究 (CREACTIVE)，以及急性儿科 TBI 试验 (ADAPT) 的方法和决策。此外，使用来自 TRACK-TBI 试点研究的数据（n  = 586) 和评估丙戊酸盐 (VPA) 治疗创伤后癫痫的完整临床试验 ( n  = 379) 我们提供了典型缺失数据模式的真实示例以及应用统计技术来减轻缺失的影响数据，以便从正在进行的临床研究中得出合理的结论。