TY - JOUR

T1 - Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis

AU - Terluin, Berend

AU - Trigg, Andrew

AU - Fromy, Piper

AU - Schuller, Wouter

AU - Terwee, Caroline B.

AU - Bjorner, Jakob B.

N1 - Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

PY - 2024

Y1 - 2024

N2 - Purpose: The minimal important change (MIC) is defined as the smallest within-individual change in a patient-reported outcome measure (PROM) that patients on average perceive as important. We describe a method to estimate this value based on longitudinal confirmatory factor analysis (LCFA). The method is evaluated and compared with a recently published method based on longitudinal item response theory (LIRT) in simulated and real data. We also examined the effect of sample size on bias and precision of the estimate. Methods: We simulated 108 samples with various characteristics in which the true MIC was simulated as the mean of individual MICs, and estimated MICs based on LCFA and LIRT. Additionally, both MICs were estimated in existing PROMIS Pain Behavior data from 909 patients. In another set of 3888 simulated samples with sample sizes of 125, 250, 500, and 1000, we estimated LCFA-based MICs. Results: The MIC was equally well recovered with the LCFA-method as using the LIRT-method, but the LCFA analyses were more than 50 times faster. In the Pain Behavior data (with higher scores indicating more pain behavior), an LCFA-based MIC for improvement was estimated to be 2.85 points (on a simple sum scale ranging 14–42), whereas the LIRT-based MIC was estimated to be 2.60. The sample size simulations showed that smaller sample sizes decreased the precision of the LCFA-based MIC and increased the risk of model non-convergence. Conclusion: The MIC can accurately be estimated using LCFA, but sample sizes need to be preferably greater than 125.

AB - Purpose: The minimal important change (MIC) is defined as the smallest within-individual change in a patient-reported outcome measure (PROM) that patients on average perceive as important. We describe a method to estimate this value based on longitudinal confirmatory factor analysis (LCFA). The method is evaluated and compared with a recently published method based on longitudinal item response theory (LIRT) in simulated and real data. We also examined the effect of sample size on bias and precision of the estimate. Methods: We simulated 108 samples with various characteristics in which the true MIC was simulated as the mean of individual MICs, and estimated MICs based on LCFA and LIRT. Additionally, both MICs were estimated in existing PROMIS Pain Behavior data from 909 patients. In another set of 3888 simulated samples with sample sizes of 125, 250, 500, and 1000, we estimated LCFA-based MICs. Results: The MIC was equally well recovered with the LCFA-method as using the LIRT-method, but the LCFA analyses were more than 50 times faster. In the Pain Behavior data (with higher scores indicating more pain behavior), an LCFA-based MIC for improvement was estimated to be 2.85 points (on a simple sum scale ranging 14–42), whereas the LIRT-based MIC was estimated to be 2.60. The sample size simulations showed that smaller sample sizes decreased the precision of the LCFA-based MIC and increased the risk of model non-convergence. Conclusion: The MIC can accurately be estimated using LCFA, but sample sizes need to be preferably greater than 125.

KW - Longitudinal confirmatory factor analysis

KW - Longitudinal item response theory

KW - Meaningful change threshold

KW - Minimal important change

KW - Patient-reported outcome measure

KW - Transition ratings

U2 - 10.1007/s11136-023-03577-w

DO - 10.1007/s11136-023-03577-w

M3 - Journal article

C2 - 38151593

AN - SCOPUS:85180704142

VL - 33

SP - 963

EP - 973

JO - Quality of Life Research

JF - Quality of Life Research

SN - 0962-9343

ER -