Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Division of Rheumatology, Mayo Clinic, Rochester, MN.
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN.
J Arthroplasty. 2021 Oct;36(10):3358-3361. doi: 10.1016/j.arth.2021.04.014. Epub 2021 Apr 22.
Time to event data occur commonly in orthopedics research and require special methods that are often called "survival analysis." These data are complex because both a follow-up time and an event indicator are needed to correctly describe the occurrence of the outcome of interest. Common pitfalls in analyzing time to event data include using methods designed for binary outcomes, failing to check proportional hazards, ignoring competing risks, and introducing immortal time bias by using future information. This article describes the concepts involved in time to event analyses as well as how to avoid common statistical pitfalls. Please visit the followinghttps://youtu.be/QNETrx8B6IUandhttps://youtu.be/8SBoTr9Jy1Qfor videos that explain the highlights of the paper in practical terms.
在骨科研究中,时间事件数据很常见,需要使用特殊的方法,这些方法通常被称为“生存分析”。这些数据很复杂,因为需要随访时间和事件指标来正确描述感兴趣结局的发生。分析时间事件数据时常见的陷阱包括使用设计用于二项结局的方法、未能检查比例风险、忽略竞争风险以及通过使用未来信息引入不朽时间偏差。本文描述了时间事件分析中涉及的概念,以及如何避免常见的统计陷阱。请访问以下链接:https://youtu.be/QNETrx8B6IU 和 https://youtu.be/8SBoTr9Jy1Q,以观看以实际方式解释本文要点的视频。