Notes

Celecoxib as well as diclofenac hepatic standing in the reputation or perhaps lack of rebamipide.
OBJECTIVES To map the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) onto the EQ-5D-5L in patients with hip or knee osteoarthritis (OA). METHODS A prospective observational study was conducted on 758 patients with hip or knee OA who completed the EQ-5D-5L and WOMAC questionnaires, of whom 644 completed them both again 6 months later. Baseline data were used to derive mapping functions. Generalized additive models were used to identify to which powers the WOMAC subscales should be raised to achieve a linear relationship with the response. (E/Z)-BCI ic50 For the modeling, general linear models (GLM), Tobit models, and beta regression models were used. Age, sex, and affected joints were also considered. Preferred models were selected based on Akaike and Bayesian information criteria, adjusted R2, mean absolute error (MAE), and root mean squared error (RMSE). The functions were validated with the follow-up data using MAE, RMSE, and the intraclass correlation coefficient. RESULTS The preferred models were a GLM with Pain2+Pain3+Function+Pain·Function as covariates and a beta model with Pain3+Function+Function2+Function3 as covariates. The adjusted R2 were similar (0.6190 and 0.6136, respectively). The predictive performance of these models in the validation sample was similar and both models showed an overprediction for health states worse than death. CONCLUSION To our knowledge, these are the first functions mapping the WOMAC onto the EQ-5D-5L in patients with hip or knee OA. They showed an acceptable fit and precision and could be very useful for clinicians and researchers when cost-effectiveness studies are needed and generic preference-based health-related quality of life instruments to derive utilities are not available. OBJECTIVES The Patient-Reported Outcomes Measurement Information System® (PROMIS) Profile instruments measure health status on 8 PROMIS domains. The PROMIS-Preference (PROPr) score provides a preference-based summary score for health states defined by 7 PROMIS domains. The Profile and PROPr share 6 domains; PROPr has 1 unique domain (Cognitive Function-Abilities), and the Profile has 2 unique domains (Anxiety and Pain Intensity). We produce an equation for calculating PROPr utility scores with Profile data. METHODS We used data from 3982 members of US online survey panels who have scores on all 9 PROMIS domains. We used a 70%/30% split for model fit/validation. Using root-mean-square error and mean error on the utility scale, we compared models for predicting the missing Cognitive Function score via (A) the population average; (B) a score representing excellent cognitive function; (C) a score representing poor cognitive function; (D) a score predicted from linear regression of the 8 profile domains; and (E) a score predicted from a Bayesian neural network of the 8 profile domains. RESULTS The mean errors in the validation sample on the PROPr scale (which ranges from -0.022 to 1.00) for the models were (A) 0.025, (B) 0.067, (C) -0.23, (D) 0.018, and (E) 0.018. The root-mean-square errors were (A) 0.097, (B) 0.12, (C) 0.29, (D) 0.095, and (E) 0.094. CONCLUSION Although the Bayesian neural network had the best root-mean-square error for producing PROPr utility scores from Profile instruments, linear regression performs almost as well and is easier to use. We recommend the linear model for producing PROPr utility scores for PROMIS Profiles. OBJECTIVES The Diary for Irritable Bowel Syndrome Symptoms-Constipation (DIBSS-C) has been developed to assess the core signs and symptoms of irritable bowel syndrome with constipation (IBS-C). This article presents the psychometric evaluation of the DIBSS-C abdominal score. METHODS Data for these analyses are from a multicenter phase IIb study in IBS-C patients (NCT02559206). Subjects completed a number of assessments via handheld electronic diary throughout the study. The analyses used the intent-to-treat population and were blinded to randomized treatment group. The analyses evaluated the reliability, validity, and responsiveness of the DIBSS-C abdominal score; identified an appropriate scoring algorithm; and determined thresholds for interpreting clinically meaningful changes at the individual level. RESULTS The correlations between the DIBSS-C abdominal symptom items (ie, abdominal pain, discomfort, and bloating) were strong (>0.75). Cronbach's alpha for the abdominal symptom severity items was very strong (.94), indicating that the 3 abdominal symptom items produce a reliable score. The intraclass correlation coefficient for the abdominal score was 0.82, exceeding the threshold of 0.70 and indicating good test-retest reliability. Guyatt's responsiveness statistic values all exceeded the threshold for a large effect of 0.80, so the DIBSS-C abdominal score can be considered highly responsive to change. Triangulation across 3 sets of anchor-based analyses indicated that a threshold of -2.0 points on the abdominal score is an appropriate threshold for identifying meaningful change. CONCLUSIONS Overall, this study provides evidence that the DIBSS-C abdominal score is valid, reliable, responsive to change, and interpretable for assessing treatment benefit in patients with IBS-C. OBJECTIVE To increase the understanding of patient-centered care (PCC) and address the need for cross-cutting quality cancer care measures that are relevant to both patients and providers. METHODS An exploratory factor analysis (EFA) was performed on a short version of the Patients and the Cancer Care Experience Survey, a patient-reported measure of perceived importance of social, emotional, physical, and informational aspects of care, administered to adult patients (n = 104) at a National Cancer Institute-designated comprehensive cancer center. Relationships between PCC dimensions and patient characteristics were also assessed. Principal axis factoring was applied and bivariate analyses were performed using Wilcoxon rank-sum tests. RESULTS Most of our sample was over 60 years old (63.4%), female (57.4%), and white (74.2%), with either breast (41.2%) or prostate cancer (27.5%). A 5-factor model was identified (1) quality of life (α = .91), (2) provider social support (α = .83), (3) psychosocial needs (α = .91), (4) nonprovider social support (α = .
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