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开发和比较四个贝叶斯网络家族对二进制结果的自相关模型:估计涉及采用医疗技术的对等效应
Guanqing Chen1, A James O'Malley2,3
1Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
本研究介绍了对二进制结果的四种网络自相关模型,解决了社交网络分析中的差距. 关于医院采用机器人手术的研究结果显示,没有统计学上显著的同行效应.
科学领域:
- 社交网络分析 社交网络分析
- 统计建模 统计建模
- 医疗保健服务研究 医疗服务研究
背景情况:
- 网络自相关模型在社交网络分析中被广泛使用.
- 对二进制依赖变量的模型较不发达,限制了对二进制结果的社会影响的分析.
- 了解同行影响对于采用新技术至关重要.
研究的目的:
- 为二进制依赖变量开发和评估四种网络自相关模型.
- 在医院中调查机器人手术采用的同行效应.
- 比较贝叶斯估计方法和模型合适标准.
主要方法:
- 为二进制结果 (直接和间接影响) 提出了四种网络自相关模型.
- 在同行效应参数上使用贝叶斯估计与统一的先验.
- 利用模拟研究进行精度和稳定性评估.
- 应用模型到美国医院患者共享网络使用医疗保险数据 (2016-2017).
主要成果:
- 间接的同行影响 (采用倾向) 是积极的,但不显著.
- 直接的同行效应 (采用概率) 降低了,但没有显著.
- 使用统一先验的贝叶斯估计显示了与替代先验可比的结果.
- 偏差信息标准和后预测p值用于模型比较和评估.
结论:
- 开发的模型为分析社交网络中的二元结果提供了一个框架.
- 在研究期间,美国医院在采用机器人手术方面没有发现统计学意义上的同行效应.
- 可能需要进一步的研究来探索影响医疗保健机构采用技术的因素.
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