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Journal Article
Research Support, N.I.H., Extramural
Establishing a common metric for self-reported pain: linking BPI Pain Interference and SF-36 Bodily Pain Subscale scores to the PROMIS Pain Interference metric.
Quality of Life Research 2015 October
PURPOSE: The study purposes were to mathematically link scores of the Brief Pain Inventory Pain Interference Subscale and the Short Form-36 Bodily Pain Subscale (legacy pain interference measures) to the NIH Patient-Reported Outcome Measurement Information System (PROMIS(®)) Pain Interference (PROMIS-PI) metric and evaluate results.
METHODS: Linking was accomplished using both equipercentile and item response theory (IRT) methods. Item parameters for legacy items were estimated on the PROMIS-PI metric to allow for pattern scoring. Crosswalk tables also were developed that associated raw scores (summed or average) on legacy measures to PROMIS-PI scores. For each linking strategy, participants' actual PROMIS-PI scores were compared to those predicted based on their legacy scores. To assess the impact of different sample sizes, we conducted random resampling with replacement across 10,000 replications with sample sizes of n = 25, 50, and 75.
RESULTS: Analyses supported the assumption that all three scales were measuring similar constructs. IRT methods produced marginally better results than equipercentile linking. Accuracy of the links was substantially affected by sample size.
CONCLUSIONS: The linking tools (crosswalks and item parameter estimates) developed in this study are robust methods for estimating the PROMIS-PI scores of samples based on legacy measures. We recommend using pattern scoring for users who have the necessary software and score crosswalks for those who do not.
METHODS: Linking was accomplished using both equipercentile and item response theory (IRT) methods. Item parameters for legacy items were estimated on the PROMIS-PI metric to allow for pattern scoring. Crosswalk tables also were developed that associated raw scores (summed or average) on legacy measures to PROMIS-PI scores. For each linking strategy, participants' actual PROMIS-PI scores were compared to those predicted based on their legacy scores. To assess the impact of different sample sizes, we conducted random resampling with replacement across 10,000 replications with sample sizes of n = 25, 50, and 75.
RESULTS: Analyses supported the assumption that all three scales were measuring similar constructs. IRT methods produced marginally better results than equipercentile linking. Accuracy of the links was substantially affected by sample size.
CONCLUSIONS: The linking tools (crosswalks and item parameter estimates) developed in this study are robust methods for estimating the PROMIS-PI scores of samples based on legacy measures. We recommend using pattern scoring for users who have the necessary software and score crosswalks for those who do not.
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