使用模拟和真实数据对时间序列的结合点分析方法的评估
Lucie Noé1, Zaba Valtuille1, Emilie Lanoy2
1Center of Clinical Investigations, INSERM CIC1426, Robert Debré University Hospital, APHP.Nord, Paris, France; Université Paris Cité, UMR 1343, Perinatal and Pediatric Pharmacology and Therapeutic Assessment, Paris, France.
Journal of clinical epidemiology
|September 3, 2025
概括
这项研究比较了Joinpoint回归计划 (JRP) 和R"细分"方案进行趋势分析. 在没有自身相关性的情况下,R的特异性更高,而与自身相关性医疗数据相比,JRP的表现更好.
科学领域:
- 生物统计学
- 流行病学
- 医疗服务研究
背景情况:
- 结点回归 (JR) 对于识别时间序列数据的趋势变化至关重要.
- 选择合适的JR软件对于准确分析医疗保健趋势至关重要.
研究的目的:
- 为了比较连接点回归程序 (JRP) 和R"细分"包在检测连接点的性能.
- 使用不同特征的模拟数据和现实世界儿童精神健康住院数据来评估软件性能.
主要方法:
- 模拟了1000个数据集与受控的自关联,趋势变化和连接点位置.
- 分析了儿童心理健康月度住院比例 (2016-2023年).
- 评估准确性,特异性,置信区间覆盖范围和月度百分比变化 (MPC) 覆盖范围.
主要成果:
- 在没有自相关的模拟中,R的特异性高于JRP (92.7%).
- 随着趋势的变化和自身相关性,与R相比,JRP的准确性和置信区间覆盖率更好.
- 对儿童精神卫生住院的分析显示,JRP和R之间检测到的结合点和平均月度百分比变化存在差异.
结论:
- 对于缺乏剩余自相关性的数据集,R"分段"包是合适的.
- 建议使用JRP分析自身相关的医疗保健数据或没有显著趋势变化的数据.
- 用于结合点回归的软件选择应与可靠趋势分析的特定数据集特征保持一致.
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