通过模拟对网络非独立性的统计纠正进行评估
Luke J Matthews1, Megan S Schuler2, Raffaele Vardavas3
1RAND Corporation, 20 Park Plaza #920, Boston, MA 02216, USA.
分析卫生服务中的社会影响需要考虑网络的相互依赖性. 网络节点的随机抽样显示了最低的假阳性率,但减少了统计能力,表明需要改进方法.
科学领域:
- 社会流行病学社会流行病学
- 医疗服务研究 医疗服务研究
- 网络分析 网络分析
背景情况:
- 社会过程和环境显著影响健康行为.
- 网络中的社会影响,例如医疗保健提供者之间的社会影响,可以影响健康结果.
- 统计方法通常假定数据独立性,而在社交网络中,这种独立性被侵犯了.
研究的目的:
- 系统地比较常用的统计方法来解决因社会影响而导致的网络不独立问题.
- 评估这些方法在假阳性率,系数偏差和统计能力方面的表现.
主要方法:
- 模拟网络数据被生成以模仿社会影响.
- 我们比较了八种统计方法来计算网络非独立性.
- 绩效指标包括假阳性率,公正的系数估计和统计能力.
主要成果:
- 没有一种测试方法达到0.05名义假阳性率.
- 网络节点的随机抽样显示了最低的假阳性率.
- 随机抽样方法导致统计能力降低.
结论:
- 目前在医疗服务研究中解决网络非独立性的方法存在局限性.
- 需要进一步的方法发展,以平衡分析社会影响力的准确性和能力.
- 准确分析与健康相关的社会影响需要强大的统计方法.
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