| Title |
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
|
|---|---|
| Published in |
Quality of Life Research, April 2015
|
| DOI | 10.1007/s11136-015-0987-6 |
| Pubmed ID | |
| Authors |
Karon F. Cook, Benjamin D. Schalet, Michael A. Kallen, Joshua P. Rutsohn, David Cella |
| Abstract |
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. 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. 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. 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 2% |
| United States | 1 | 2% |
| Netherlands | 1 | 2% |
| Unknown | 62 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 23% |
| Student > Ph. D. Student | 11 | 17% |
| Student > Master | 6 | 9% |
| Professor | 5 | 8% |
| Student > Doctoral Student | 4 | 6% |
| Other | 9 | 14% |
| Unknown | 15 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 26% |
| Psychology | 10 | 15% |
| Nursing and Health Professions | 4 | 6% |
| Neuroscience | 2 | 3% |
| Economics, Econometrics and Finance | 2 | 3% |
| Other | 9 | 14% |
| Unknown | 21 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 September 2015.
All research outputs
#14,222,419
of 22,800,560 outputs
Outputs from Quality of Life Research
#1,443
of 2,844 outputs
Outputs of similar age
#139,424
of 265,112 outputs
Outputs of similar age from Quality of Life Research
#16
of 52 outputs
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