用模拟,哈雷尔-戴维斯估计器和非线性定量回归推导的单音符识别分数的信心极限的准确性和一致性
Vijaya Kumar Narne1,2, Dhanya Mohan3, Sruthi Das Avileri3
1Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61481, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|July 13, 2024
概括
非线性定量回归 (nQR) 方法准确地估计了与纯色调平均值相比的语音识别得分. 这种听力学工具提供了更好的准确性和一致性,而不需要任意分组.
科学领域:
- 听力学 听力学是指听力学.
- 语音感知 语音感知
- 统计建模 统计建模
背景情况:
- 评估最大语音识别分数 (PBmax) 相对于纯色调平均值 (PTA) 值对于听力学诊断和康复至关重要.
- 需要对PBmax的95%置信限值 (CL) 进行准确的估计,以确定基于PTA的得分是否低于预期.
- 本研究评估了估计PBmax分数95%CL的三种方法.
研究的目的:
- 为了比较三个方法的准确性和一致性,用于估计最大语音识别分数 (PBmax) 的95%置信限 (CL) .
- 确定临床听力学实践中最可靠的方法.
主要方法:
- 我们比较了三种方法:模拟方法,Harrell-Davis (HD) 估计器和非线性定量回归 (nQR).
- HD和nQR方法是无分布的,不需要预定义的纯色平均 (PTA) 子组.
- 通过将每个方法应用于随机数据样本并预测剩余数据来评估准确性和一致性.
主要成果:
- 模拟方法超过了目标5%的错误阳性率,6.7-8.2%的得分低于估计的95%CL.
- 哈雷尔-戴维斯 (HD) 和非线性定量回归 (nQR) 方法表现出很好的准确性,大约5%的得分低于95%的CL.
- 所有方法的估计都显示出相似的一致性.
结论:
- 推使用非线性定量回归 (nQR) 方法来估计最高语音识别分数的95%置信限.
- 与模拟方法相比,nQR提供了更高的准确性和一致性,并且不需要任意纯色平均子组.
- 这种方法可以通过提供更可靠的语音识别性能基准来改善听力学诊断和康复.
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