BackgroundThe minimally important difference (MID) refers to the smallest change that is sufficiently meaningful to carry implications for patients’ care. MIDs are necessary to guide the interpretation of scores. This study estimated MID for the Patient Reported Outcomes Measurement Information System (PROMIS) pain interference (PI).MethodsStudy instruments were administered to 414 people who participated in two studies that included treatment with low back pain (LBP; n=218) or depression (n=196). Participants with LBP received epidural steroid injections and participants with depression received antidepressants, psychotherapy, or both. MIDs were estimated for the changes in LBP. MIDs were included only if a priori criteria were met (ie, sample size ≥10, Spearman correlation ≥0.3 between anchor measures and PROMIS-PI scores, and effect size range =0.2–0.8). The interquartile range (IQR) of MID estimates was calculated.ResultsThe IQR ranged from 3.5 to 5.5 points. The lower bound estimate of the IQR (3.5) was greater than mean of standard error of measurement (SEM) both at time 1 (SEM =2.3) and at time 2 (SEM =2.5), indicating that the estimate of MID exceeded measurement error.ConclusionBased on our results, researchers and clinicians using PROMIS-PI can assume that change of 3.5 to 5.5 points in comparisons of mean PROMIS-PI scores of people with LBP can be considered meaningful.

The current study proposes novel methods to predict multistage testing (MST) performance without conducting simulations. This method, called MST test information, is based on analytic derivation of standard errors of ability estimates across theta levels. We compared standard errors derived analytically to the simulation results to demonstrate the validity of the proposed method in both measurement precision and classification accuracy. The results indicate that the MST test information effectively predicted the performance of MST. In addition, the results of the current study highlighted the relationship among the test construction, MST design factors, and MST performance.

Results indicate that the PLUS-M computerized adaptive test is most efficient, and differences in scores between administration methods are minimal. The main advantage of the computerized adaptive test was more reliable scores at higher levels of mobility compared to short forms. Clinical relevance Health-related item banks, like the Prosthetic Limb Users Survey of Mobility (PLUS-M), can be administered by computerized adaptive testing (CAT) or as fixed-length short forms (SFs). Results of this study will help clinicians and researchers decide whether they should invest in a CAT administration system or whether SFs are more appropriate.

This study examined the accuracy of depression cross-walk tables in a sample of people with multiple sclerosis (MS). The tables link scores of two commonly used depression measures to the Patient Reported Outcome Measurement Information System Depression (PROMIS-D) scale metric. We administered the 8-item PROMIS-D (Short-Form 8b; PROMIS-D-8), the 20-item Center for Epidemiologic Studies Depression Scale (CESD-20), and the 9-item Patient Health Questionnaire (PHQ-9) to 459 survey participants with MS. We examined correlations between actual PROMIS-D-8 scores and the scores predicted by cross-walks based on PHQ-9 and CESD-20 scores. Intraclass correlation coefficients were used to assess correspondence. Consistency in severity classification was also calculated. Finally, we used Bland-Altman plots to graphically examine the levels of agreement. The correlations between actual and cross-walked PROMIS-D-8 scores were strong (CESD-20 = .82; PHQ-9 = .74). The intraclass correlation was moderate (.77). Participants were consistently classified as having or not having at least moderate depressive symptoms by both actual and cross-walked scores derived from the CESD-20 (90%) and PHQ-9 (85%). Bland-Altman plots suggested the smaller differences between actual and cross-walked scores with greater-than-average depression severity. PROMIS cross-walk tables can be used to translate depression scores of people with MS to the PROMIS-D metric, promoting continuity with previous research.

Background Children with chronic conditions often experience numerous symptoms, but few research studies examine patterns of symptoms and quality of life (QoL) indicators. Objective To examine if reliable latent classes of children with chronic medical conditions can be identified based on the clustering of symptoms and QoL indicators. Methods Structured interviews were conducted with children ages 9 to 21 living with chronic medical conditions (N = 90). Multiple symptoms (e.g., pain, sleep, fatigue, and depression) and QoL indicators (e.g., life satisfaction and social support) were measured. Physical health and emotional, social, and school functioning were measured using the Pediatric Quality of Life Inventory (PedsQL). Latent class analysis was used to classify each child into a latent class whose members report similar patterns of responses. Results A three-class solution had the best model fit. Class 1 (high-symptom group; n = 15, 16.7%) reported the most problems with symptoms and the lowest scores on the QoL indicators. Class 2 (moderate-symptom group; n = 39, 43.3%) reported moderate levels of both symptoms and QoL indicators. Class 3 (low-symptom group; n = 36, 40.0%) reported the lowest levels of symptoms and the highest scores on the QoL indicators. Conclusions The three latent classes identified in this study were distributed along the severity continuum. All symptoms and QoL indicators appeared to move in the same direction (e.g. worse symptoms with lower QoL). The PedsQL psychosocial health summary score (combining emotional, social, and school functioning scores) discriminated well between children with different levels of disease burden.