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使用后勤回归的临床预测模型中连续预测因子的处理不良:系统性审查
Jie Ma1, Paula Dhiman1, Cathy Qi2
1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, United Kingdom.
大多数临床预测模型错误地处理连续预测因子,将它们分类,尽管有反对它的建议. 很少有研究评估线性或使用非线性方法,影响预测准确度.
科学领域:
- 临床流行病学临床流行病学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 开发准确的临床预测模型需要适当处理连续预测器.
- 错误地指定连续预测器的功能形式可以降低模型的预测准确性.
- 本研究检查了临床预测模型开发中处理连续预测因子的当前做法.
研究的目的:
- 审查如何在研究开发临床预测模型的研究中处理连续预测因素.
- 确定用于解决连续预测器非线性问题的方法的普及率.
- 突出推实践与实际应用之间的差异.
主要方法:
- 在PubMed系统搜索使用对二进制结果的逻辑回归的临床预测模型研究.
- 包括2020年7月1日至30日期间发表的研究.
- 审查了118项研究,以评估连续预测器的处理.
主要成果:
- 只有15%的研究 (18/118) 评估了线性或处理了非线性.
- 分类,主要是二分化,是连续预测器最常用的方法 (56.8%).
- 在处理非线性问题的研究中,分别在39%和33%的研究中使用了转换和splines.
结论:
- 大多数开发临床预测模型的研究继续对连续预测因素进行分类,这与最佳实践相反.
- 评估线性或解释非线性方法的采用率很低.
- 需要方法指导来改善在模型开发中处理连续预测量的方法.
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