Modeling general populations’ reference data for PROMIS item banks (Physical Functioning, Upper Extremities, and Pain interference) in multiple countries using quantile regression.

reference data


Aims: The use of PROs in research and clinical practice requires availability of appropriate and relevant comparison data. We aim to model age, and sex-specific reference values for the PROMIS Physical Function (PF), Upper Extremity (UE), and Pain Interference (PI) scales in populations age 50 and older in Germany, the UK, and the US.

Methods: We collected PROMIS PF, UE, and PI data via telephone interviews from the general population in Germany (N = 921), the UK (N = 905), and the US (N = 900). We investigated differential item functioning (DIF) between countries using iterative hybrid ordinal logistic regression. To account for the measurement error of latent estimates and to obtain a continuous distribution of the latent variable, we imputed 25 data sets with plausible values. Each latent estimate was replaced by a random value drawn from the individual latent variable posterior distribution approximated by a normal distribution. We then utilized quantile regressions to model the 1st, 5th, 10th–90th, 95th, and 99th percentiles and their respective standard errors in each dataset based on different combinations of predictors (age, sex, country). According to Rubin’s rules, the estimated percentiles and corresponding standard errors were pooled across the imputed datasets to provide the respective reference values.

Results: Three items from the PROMIS PF scale showed negligible DIF by country, indicating that all scales are valid for inter-country comparisons. Median regressions revealed significant effects of age (PF: b𝜏=.50= -0.33; PI: b𝜏=50= 0.08), sex (PF: b𝜏=50= -3.22; PI: b𝜏=50= 1.62) and country, indicating that stratification of reference data is warranted. The PF and UE scales showed considerable ceiling effects in all countries aged 50-69. For PI, this applied to all ages.

Conclusions: This paper illustrates a novel approach to model reference values for PROMIS measures based on individual patients’ characteristics. Substantial differences in the PROMIS scores between sex, age, and countries highlight the importance of such patient-specific reference values, enabling clinicians to utilize personalized reference values. Due to the use of plausible value imputation, the obtained population reference values can be compared to data collected with other PROMIS short forms or computer-adaptive tests.

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