Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
Pediatric Pulmonology, University of North Carolina, Chapel Hill, North Carolina, USA.
Stat Med. 2021 May 30;40(12):2765-2782. doi: 10.1002/sim.8927. Epub 2021 Mar 4.
Modeling recurrent event data with multiple event types has drawn interest in recent biomedical studies due to its flexibility for understanding different risk factors for multiple recurrent event processes. However, in such data type, missing event type appears frequently because of various reasons such as recording ignorance or resource limitation. In this study, we aim to propose an inverse probability weighted estimation that is commonly used in the missing data literature to correct possibly biased estimation by a complete-case analysis. This approach is not limited to a specific form of the recurrent event model. We derive the large sample theory in a general form. We demonstrate that our approach can be applied to either multiplicative or additive rates model with practical sample size via comprehensive simulations. Nonmucoid and mucoid Pseudomonas aeruginosa infections of 14 888 patients in 2016 Cystic Fibrosis Foundation Patient Registry data are analyzed to show that, without including 12% events with missing event type in the analysis, several factors may be misidentified as risk factors for the nonmucoid type of infections.
由于能够理解多种复发事件过程的不同风险因素，因此对具有多种事件类型的复发事件数据进行建模最近引起了生物医学研究的兴趣。但是，在这种数据类型中，由于记录疏忽或资源限制等各种原因，经常会出现缺失的事件类型。在这项研究中，我们旨在提出一种逆概率加权估计方法，该方法常用于缺失数据文献中，以纠正完整病例分析可能产生的有偏估计。该方法不限于特定形式的复发事件模型。我们以一般形式推导出大样本理论。我们通过综合模拟证明，我们的方法可以应用于乘法或加法速率模型，并且在实际样本量下也可行。通过分析 2016 年囊性纤维化基金会患者登记处 14888 名患者的非黏液型和黏液型铜绿假单胞菌感染数据，结果表明，如果在分析中不包括 12％缺失事件类型的事件，则可能会错误识别出一些因素是导致非黏液型感染的危险因素。
