Abstract
The reliable change index has been used to evaluate the significance of individual change in health-related quality of life. We estimate reliable change for two measures (physical function and emotional distress) in the Patient-Reported Outcomes Measurement Information System (PROMIS®) 29-item health-related quality of life measure (PROMIS-29 v2.1). Using two waves of data collected 3 months apart in a longitudinal observational study of chronic low back pain and chronic neck pain patients receiving chiropractic care, and simulations, we compare estimates of reliable change from classical test theory fixed standard errors with item response theory standard errors from the graded response model. We find that unless true change in the PROMIS physical function and emotional distress scales is substantial, classical test theory estimates of significant individual change are much more optimistic than estimates of change based on item response theory.
Highlights
The reliable change index has been used to evaluate the significance of individual change in healthrelated quality of life
We find that unless true change in the PROMIS physical function and emotional distress scales is substantial, classical test theory estimates of significant individual change are much more optimistic than estimates of change based on item response theory
Two types of heterogeneity of treatment effects (HTEs) approaches have been used recently to separate patients within Randomized controlled clinical trials (RCTs) based on variation in benefits: (1) multi-variable modeling predicting the risk for an outcome (“risk-modeling”) and (2) evaluating interactions between treatment assignment and baseline covariates (“effect-modeling”)
Summary
The reliable change index has been used to evaluate the significance of individual change in healthrelated quality of life. We estimate reliable change for two measures (physical function and emotional distress) in the Patient-Reported Outcomes Measurement Information System (PROMIS®) 29-item healthrelated quality of life measure (PROMIS-29 v2.1). Two types of heterogeneity of treatment effects (HTEs) approaches have been used recently to separate patients within RCTs based on variation in benefits: (1) multi-variable modeling predicting the risk for an outcome (“risk-modeling”) and (2) evaluating interactions between treatment assignment and baseline covariates (“effect-modeling”). These approaches have been employed to evaluate clinical outcomes such as fractures, onset of diabetes, and mortality (Kent et al, 2018). The U.S Food and Drug Administration guidance document recommended identifying responders
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