Comparison of raw and regression approaches to capturing change on patient-reported outcome measures.

Abstract

Patient-reported outcome (PRO) analyses often involve calculating raw change scores, but limitations of this approach are well documented. Regression estimators can incorporate information about measurement error and potential covariates, potentially improving change estimates. Yet, adoption of these regression-based change estimators is rare in clinical PRO research. Both simulated and PROMIS® pain interference items were used to calculate change employing three methods: raw change scores and regression estimators proposed by Lord and Novick (LN) and Cronbach and Furby (CF). In the simulated data, estimators' ability to recover true change was compared. Standard errors of measurement (SEM) and estimation (SEE) with associated 95% confidence limits were also used to identify criteria for significant improvement. These methods were then applied to real-world data from the PROMIS® study. In the simulation, both regression estimators reduced variability compared to raw change scores by almost half. Compared to CF, the LN regression better recovered true simulated differences. Analysis of the PROMIS® data showed similar themes, and change score distributions from the regression estimators showed less dispersion. Using distribution-based approaches to calculate thresholds for significant within-patient change, smaller changes could be detected using both regression estimators. These results suggest that calculating change using regression estimates may result in more increased measurement sensitivity. Using these scores in lieu of raw differences can help better identify individuals who experience real underlying change in PROs in the course of a trial, and enhance the established methods for identifying thresholds for meaningful within-patient change in PROs.