在轴性脊椎关节炎研究中使用的常见复合分数结果的缺失组件信息的处理,当完整病例分析是不偏见的
Christos Polysopoulos1, Stylianos Georgiadis2, Lykke Midtbøll Ørnbjerg2
1Statistics Group, Swiss Clinical Quality Management (SCQM) Foundation, Aargauerstrasse 250, Zurich, 8048, Switzerland.
BMC medical research methodology
|February 28, 2025
概括
多重归算 (MI) 是处理轴性脊柱关节炎研究复合分数中缺少数据的最佳方法,提供了公正和精确. 然而,如果正确实施,完整案例 (CC) 分析仍然是一个有效的选择.
科学领域:
- 类风湿病学 类风湿病学
- 生物统计学 生物统计学
- 健康 结果 研究 研究 结果
背景情况:
- 在轴性脊椎关节炎 (axSpA) 研究中的复合得分通常有缺失的组件数据.
- 完整案例 (CC) 分析是标准的,但在缺少数据时可能不是最佳的.
- 评估处理缺失数据的方法对于准确的复合分数估计至关重要.
研究的目的:
- 为了比较处理缺失组件数据的不同方法在axSpA复合分数中的性能.
- 评估与CC分析对应的归算方法的偏差,差异和覆盖范围.
- 确定最可靠的方法来估计BASDAI,BASFI和ASDAS-CRP的平均值.
主要方法:
- 评估的个人平均推算 (IMI),修改公式 (MF),整体平均推算 (OMI) 和多重推算 (MI).
- 使用欧洲患者登记数据的分析方法和模拟.
- 与完整案例 (CC) 分析进行偏差,差异和覆盖的比较方法.
主要成果:
- 发现IMI和MF方法本质上是有偏见的.
- OMI提供了一个公正的平均值,但导致了覆盖范围的问题.
- 多重推算 (MI) 提高了准确性,并提供了公正的手段,适当地覆盖了有限的缺失.
- 完整案例 (CC) 分析,当没有错误时,是一个有效的替代方案.
结论:
- 多重推算 (MI) 是唯一一种成功地保持了CC的公正性,同时提高了axSpA复合分数的精度的方法.
- MI的表现可能会因大量缺失而下降,并且实施错误是令人担忧的.
- 没有错误的完整案例 (CC) 分析是估计axSpA研究中的复合分数的有价值和有效选择.
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