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Estimating minimally important differences for the PROMIS pain interference scales: results from 3 randomized clinical trials.

Chen CX, Kroenke K, Stump TE, Kean J, Carpenter JS, Krebs EE, Bair MJ, Damush TM, Monahan PO. Estimating minimally important differences for the PROMIS pain interference scales: results from 3 randomized clinical trials. Pain. 2018 Apr 1; 159(4):775-782.

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Minimally important difference (MID) refers to the smallest meaningful difference that carries implications for patient care. Minimally important differences are necessary to help interpret patient-reported pain outcomes in research and clinical practice. The PROMIS pain interference scales were validated across diverse samples; however, more information about their MIDs could improve their interpretability. The purpose of this study was to estimate MIDs for 4 fixed-length PROMIS pain interference scales, including the 6-item Pain Short Form and the 4-, 6-, and 8-item pain interference scales used in the PROMIS profile instruments. Data were analyzed from 3 randomized controlled trials (N = 759). The 3 samples, respectively, consisted of patients with chronic low back pain (n = 261), chronic back pain or hip/knee osteoarthritis pain (n = 240), and a history of stroke (n = 258). For each sample, anchor- and distribution-based approaches were used to estimate MIDs. Standard error of measurement and effect sizes were used as distribution-based MID estimates. Anchor-based MID estimates were established by mapping PROMIS pain interference scores onto established anchor measures, including the Brief Pain Inventory, and retrospective and prospective global ratings of change. The distribution- and anchor-based MID estimates showed convergence. For the pain samples, MID estimates ranged from 2 to 3 T-score points. For the nonpain sample, MID estimates ranged from 3.5 to 4.5 T-score points. The MID estimates were comparable across the 4 fixed-length scales. These MIDs can be used to evaluate treatment effects in research and clinical care and to calculate estimates for powering clinical trials.

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