使用机器学习对未完成调查的个体和方法风险因素的表征:来自美国千年队列研究的发现
Nate C Carnes1, Claire A Kolaja2,3, Crystal L Lewis2,3
1Deployment Health Research Department, Naval Health Research Center, San Diego, CA, USA. nathan.c.carnes.mil@health.mil.
BMC medical research methodology
|July 14, 2025
概括
机器学习准确地识别了调查未完成,这是纵向研究中常见的问题. 调查设计和受访者特征等因素影响完成率,这对于公正的研究结果至关重要.
科学领域:
- 数据科学数据科学数据科学
- 流行病学 流行病学
- 调查方法 调查方法
背景情况:
- 纵向队列研究中缺少的数据可能会损害有效性和概括性.
- 调查未完成,即受访者开始但未完成,引入了潜在的偏见.
- 了解导致不完成的因素对于准确的研究至关重要.
研究的目的:
- 展示机器学习在识别调查未完成方面的能力.
- 描述与调查未完成相关的个人和方法因素.
主要方法:
- 开发了一种新的机器学习算法来检测未完成的调查.
- 分析了来自千年队列研究 (2019-2021) 的数据.
- 检查了影响未完成的个人特征和调查属性.
主要成果:
- 该算法在识别未完成者方面实现了99%的准确性.
- 15.43%的新入学者和0.29%的随访受访者是没有完成的.
- 招募工资等级,敏感问题,较少的问题和早期调查部分增加了未完成的可能性.
结论:
- 考虑到因调查未完成而导致的受访者偏见至关重要.
- 在大型队列研究中,确定了导致调查未完成的关键因素.
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