在经过匹配调整的间接比较中对不确定性的不确定性不确定吗? 一个模拟研究来比较差异估计方法.
Conor O Chandler1, Irina Proskorovsky1
1Evidence Synthesis, Modeling & Communication, Evidera, Bethesda, Maryland, USA.
Research synthesis methods
|September 26, 2024
概括
经匹配调整的间接比较 (MAIC) 需要准确的差异估计. 具有有效样本大小权重的传统估计器通常表现良好,而引导式估计在某些场景中表现不稳定.
科学领域:
- 卫生技术评估 卫生技术评估
- 生物统计学 生物统计学
- 相对有效性研究研究比较
背景情况:
- 对匹配调整的间接比较 (MAIC) 对于医疗技术评估中的对比比较至关重要,解决了基线特征失衡.
- 由于对差异估计的指导有限,准确量化MAIC匹配过程中的不确定性仍然是一个重大挑战.
研究的目的:
- 评估各种统计方法在MAIC中对差异估计的性能.
- 为了确定最可靠的方法来量化MAIC的不确定性在不同的场景.
主要方法:
- 进行了一项全面的蒙特卡洛模拟研究,评估了108种场景.
- 我们比较了四种主要差异估计方法:传统估计器 (原始和ESS权重),三明治估计器和引导.
- 在定和未定MAIC中,对二进制和时间到事件结果的覆盖概率和可变性比率来评估性能.
主要成果:
- 使用原始权重的传统估计器低估了贫困/中等人口重叠下的变化.
- 使用有效样本大小 (ESS) 权重的传统估计器在大多数场景中证明了准确的不确定性估计.
- 三明治估计器在有限样本调整时显示出改善,而引导式估计器在不良重叠和小样本大小的情况下证明不稳定.
结论:
- 在MAIC中选择差异估计方法受到样本大小,人口重叠和结果类型的影响.
- 使用ESS权重的传统估计器为MAICs的不确定性估计提供了强大的方法.
- 需要进一步的研究和精细的方法来进行可靠的差异估计,特别是在具有挑战性的场景中.
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