Optimizing the Accuracy and Precision of the Minimal Detectable Change Statistic: Secondary Analysis of Test-Retest Data from the NIH Toolbox Study.

IF 3.5 4区 医学 Q1 ORTHOPEDICS Physical Therapy Pub Date : 2024-11-22 DOI:10.1093/ptj/pzae170
Jeremy Graber, Brian J Loyd, Thomas J Hoogeboom, Caitlin J Miller, Andrew J Kittelson
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Abstract

Objective: The minimal detectable change (MDC) statistic is often used by clinicians to monitor change in patients. However, the way in which the MDC is traditionally calculated might be suboptimal in terms of accuracy and precision, potentially resulting in erroneous clinical decisions. This study compared the performance of the MDC statistic as traditionally calculated to that of 2 regression-based alternatives.

Methods: This analysis used test-retest data from adults who participated in usual walking speed (n = 169) or grip strength (n = 178) assessments as part of the NIH Toolbox Study. Three approaches for MDC calculation were compared: the traditional approach (MDCTrad), simple linear regression (MDCSLR), and generalized additive models for location, scale, and shape (MDCGAMLSS). These approaches were compared in terms of accuracy and precision across all levels of measurement and separately for initial test values above and below the median.

Results: Each of the 3 approaches accurately modeled detectable change thresholds when performance was averaged across all test values. However, MDCTrad demonstrated inaccuracies when performance was considered separately for initial test values below or above the median. For walking speed, average precision improved by 12% with MDCSLR and 16% with MDCGAMLSS, compared to MDCTrad. For grip strength, average precision improved by 3% with MDCSLR and 21% with MDCGAMLSS, compared to MDCTrad.

Conclusion: MDCSLR and MDCGAMLSS appeared to more accurately and precisely model detectable change thresholds, compared to MDCTRAD. In general, MDCGAMLSS demonstrated the best overall performance in this within-sample analysis.

Impact: Improved precision and accuracy in detectable change thresholds for walking speed or grip strength might facilitate clinicians' ability to promptly detect a decline in function and intervene and to confidently detect improvements in function over time.

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优化最小可检测变化统计量的准确性和精确度:美国国立卫生研究院工具箱研究中测试-重测数据的二次分析。
目的:临床医生经常使用最小可检测变化(MDC)统计来监测患者的变化。然而,传统的 MDC 计算方法在准确性和精确性方面可能不够理想,从而可能导致错误的临床决策。本研究比较了传统计算的 MDC 统计量与两种基于回归的替代方法的性能:本分析使用了作为美国国立卫生研究院工具箱研究一部分的成人测试-再测试数据,这些成人参加了通常的步行速度(n = 169)或握力(n = 178)评估。比较了三种计算 MDC 的方法:传统方法 (MDCTrad)、简单线性回归 (MDCSLR) 和位置、比例和形状的广义加法模型 (MDCGAMLSS)。对这些方法在所有测量水平上的准确度和精确度进行了比较,并分别对高于和低于中位数的初始测试值进行了比较:结果:当对所有测试值的表现进行平均时,3 种方法中的每一种都能准确模拟可检测的变化阈值。然而,当分别考虑初始测试值低于或高于中位数时,MDCTrad 表现出不准确性。在步行速度方面,与 MDCTrad 相比,MDCSLR 和 MDCGAMLSS 的平均精确度分别提高了 12% 和 16%。在握力方面,与 MDCTrad 相比,MDCSLR 的平均精确度提高了 3%,MDCGAMLSS 的平均精确度提高了 21%:结论:与 MDCTRAD 相比,MDCSLR 和 MDCGAMLSS 似乎能更准确、更精确地模拟可检测到的变化阈值。总体而言,MDCGAMLSS 在样本内分析中表现出最佳的整体性能:影响:提高步行速度或握力的可检测变化阈值的精确度和准确性,可能有助于临床医生及时发现功能下降并进行干预,以及有把握地发现随着时间的推移功能有所改善。
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来源期刊
Physical Therapy
Physical Therapy Multiple-
CiteScore
7.10
自引率
0.00%
发文量
187
审稿时长
4-8 weeks
期刊介绍: Physical Therapy (PTJ) engages and inspires an international readership on topics related to physical therapy. As the leading international journal for research in physical therapy and related fields, PTJ publishes innovative and highly relevant content for both clinicians and scientists and uses a variety of interactive approaches to communicate that content, with the expressed purpose of improving patient care. PTJ"s circulation in 2008 is more than 72,000. Its 2007 impact factor was 2.152. The mean time from submission to first decision is 58 days. Time from acceptance to publication online is less than or equal to 3 months and from acceptance to publication in print is less than or equal to 5 months.
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