评价得分建立中简单性的成本:酒精研究中的一个例子
Valentin Rousson1, Bastien Trächsel1, Katia Iglesias2
1Division of Biostatistics, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
简化用于预测酒精使用障碍 (AUD) 诊断的得分显示了统计性能的最小损失. 简单的总和得分可以与复杂的物流回归模型相比较,增强实现和解释.
科学领域:
- 心理学和健康科学 心理学和健康科学
- 临床心理学 临床心理学
- 心理测量 心理测量 心理测量
背景情况:
- 从问卷中开发预测分数在健康和心理学研究中很常见.
- 一个常见的挑战是平衡统计性能与实际应用的得分简单性.
研究的目的:
- 在创建预测分数时,调查统计性能和简单性之间的权衡.
- 评估简化得分版本与精细的后勤回归模型进行评估,用于预测酒精使用障碍 (AUD).
主要方法:
- 使用233名瑞士年轻男子的真实数据,将精细的后勤回归得分与简化的版本 (1-3位,总得分) 进行了比较.
- 使用了最大概率,逆向选择,LASSO和脊惩罚估计方法.
- 采用乐观度校正的启动程序来评估预测准确性 (ROC曲线下的面积 - AUC).
主要成果:
- 简化得分,特别是总分,表现几乎相同或略高于精制后勤回归得分.
- 精制和总分数的修正AUC范围分别为0.828-0.848和0.835-0.850.
- 在测试的方法中,LASSO估计方法产生了最佳的整体性能.
结论:
- 简化对二进制结果的预测得分不一定会导致显著的统计性能损失.
- 简化得分,特别是总分,在心理学和健康科学中的实施,解释和可接受性方面提供了潜在的改进.
- 经验证据支持使用总分数用于实际临床和研究应用的好处.
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