Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis
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Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis. / Terluin, Berend; Trigg, Andrew; Fromy, Piper; Schuller, Wouter; Terwee, Caroline B.; Bjorner, Jakob B.
In: Quality of Life Research, Vol. 33, 2024, p. 963–973.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 379580815