临床研究基于组的轨迹建模的最新进展
Daniel S Nagin1, Bobby L Jones2, Jonathan Elmer3
1Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA;
Annual review of clinical psychology
|February 21, 2024
概括
基于组的轨迹建模 (GBTM) 有助于识别具有相似健康旅程的患者群体. 最近的进展加强了其在临床研究中的使用,以更好地预测结果和患者分类.
科学领域:
- 临床心理学 临床心理学
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
背景情况:
- 基于组的轨迹建模 (GBTM) 是分析纵向数据的强大工具.
- 它的分类性质非常适合临床心理学和医学,有助于患者的分类.
- 然而,一些先进的功能在临床研究中仍未得到充分利用.
研究的目的:
- 审查GBTM的最新进展.
- 突出GBTM在临床研究中的不被重视的能力.
- 为选择最佳数量的轨迹组提供指导.
主要方法:
- 关于基于组的轨迹建模的最新文献的综述.
- 讨论先进的GBTM功能,包括处理磨损,联合和多轨道模型,β分布和结果预测.
- 探索确定轨迹组数量的方法.
主要成果:
- GBTM提供了高级功能,如处理非随机磨损和联合/多轨道建模.
- 测试版分发增强了建模灵活性.
- GBTM可以将轨迹与未来结果联系起来,并估计结果的概率.
- 讨论了选择轨迹组数量的方法.
结论:
- 最近的进展显著扩大了GBTM在临床研究中的实用性.
- 低估的GBTM功能可以改善患者分类和结果预测.
- 进一步采用这些先进的方法将增强临床心理学和医学.
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