使用多站点RCT预测单个站点的影响:更好的数据和方法是否产生更准确的预测?
Robert B Olsen1, Larry L Orr2, Stephen H Bell3
1George Washington Institute of Public Policy, The George Washington University, Washington, DC 20052.
概括
多站点随机对照试验 (RCT) 提供了平均影响估计. 然而,预测个别地点对政策决策的影响仍然具有挑战性,特别是当干预效应在不同地点有很大差异时.
科学领域:
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
- 流行病学 流行病学
背景情况:
- 多站点随机对照试验 (RCT) 对于公正的平均治疗效果估计至关重要.
- 对于个别地点的RCT的预测准确性,特别是为当地政策提供信息,尚不清楚.
研究的目的:
- 评估现代统计方法在基于多站点RCT数据的基础上预测单个站点的干预影响的能力.
- 在不同地点不同程度的影响异质性下评估预测准确性.
主要方法:
- 对六个多站点RCT的分析.
- 预测方法的比较,包括拉索回归和贝叶斯附加回归树 (BART) 与样本平均治疗效应.
- 包括广泛的调节变量来解释影响变化.
主要成果:
- 当不同地点的影响变化最小时,预测准确度很高.
- 没有一种测试方法能够准确地预测当存在实质性变异时的影响.
- 总体而言,BART的预测比拉索回归或样本平均治疗效应的预测更少不准确.
结论:
- 使用典型的多站点RCT数据的统计建模不足以解释或预测当干预效应大大不同时特定站点的影响.
- 当前的预测方法可能无法在异质环境中充分支持地方政策决策.
- 需要进一步的研究来提高复杂干预研究中单个地点的预测准确度.
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