Aims
A primary advantage of IRT-based patient-reported outcome measures such as PROMIS short forms and computer-adaptive tests is that each estimate of the latent trait comes with a standard error. Such measurement error needs to be acknowledged, in particular when monitoring individual patients over time. In this study, we use plausible values to account for measurement error and analyze the probability of true within-individual change.
Methods
We use a longitudinal, observational study of stable and exacerbated COPD patients (N = 185), providing PROMIS Physical Function and Fatigue T-scores over 3 months. At each measurement, we imputed 1000 plausible values from the scores’ posterior distribution. These were then used to calculate probability of true change using a pre-specified threshold such as minimally important difference supported by the literature, or \(\Delta T-score\) > 0. We demonstrate assessment of change in individuals and in groups, across different measures (Short Forms and CATs), and at various levels of confidence.
Results
Using plausible value imputation and with 95% certainty, 47.5% of participants in the exacerbated group reported less fatigue, compared with 26.5% of participants in the stable group. Comparison of Short Forms and CATs suggests that CATs have better ability to detect change compared to short forms. We also illustrate this method using an individual’s probability of change at different time points.
Conclusion
Plausible values offer a flexible way to include measurement error in analysis of individuals and on sample level. Assessment of probability of true change can complement existing distribution-based approaches and facilitates interpretation of improvement or decline.
