评估基于组的轨迹建模方法的性能,以发现不同的药物坚持模式
Awa Diop1,2, Alind Gupta3, Sabrina Mueller4
1Core Clinical Sciences Inc., Vancouver, British Columbia, Canada.
Pharmaceutical statistics
|February 8, 2024
概括
基于组的轨迹建模 (GBTM) 有效地捕捉复杂的药物坚持模式,在模拟研究中优于覆盖天数 (PDC) 和纵向K-平均值的比例.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 制药科学 制药科学
背景情况:
- 药物坚持对于患者的结果和降低死亡率至关重要.
- 药房质量联盟 (PQA) 将药物坚持确定为关键质量指标.
- 当前的坚持测量方法,如覆盖日数比例 (PDC),在捕捉动态患者行为方面存在局限性.
研究的目的:
- 评估基于组的轨迹建模 (GBTM) 在评估药物坚持方面的有效性.
- 使用模拟数据,将GBTM的性能与覆盖日数比例 (PDC) 和纵向K-平均值进行比较.
- 证明GBTM能够捕捉治疗坚持模式中的异质性.
主要方法:
- 通过使用二次函数和共变量生成的时间变化的治疗数据进行了一项模拟研究.
- 使用了三种轨迹模型,包括"猫的摇篮"和"彩虹"效果.
- 通过比较GBTM,PDC和纵向K-平均值,使用绝对偏差,方差,c统计,相对偏差和相对方差等指标来评估性能.
主要成果:
- 基于小组的轨迹建模 (GBTM) 在捕捉与PDC和纵向K-平均值相比,在捕捉各种药物坚持模式方面表现优越.
- 在所有模拟场景中,GBTM表现出较低的相对偏差和差异,即使模型被错误指定.
- 该研究强调了GBTM在分析复杂,动态的粘附数据方面的稳定性.
结论:
- 基于组的轨迹建模 (GBTM) 是一种比PDC等传统方法更有效的方法来评估药物坚持.
- GBTM能够捕捉异质性和坚持的动态变化的能力,为患者的行为提供了更细致的理解.
- 进一步应用GBTM可以改善药物使用质量评估和患者护理策略.
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