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

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.