The Localized Scleroderma Quality Of Life Instrument (LOSQI): A Disease-Specific Survey Using Anchoring Vignettes

The main goal of this project was to develop and provide validity evidence for a disease- specific quality of life survey to be used with pediatric localized scleroderma (LS) patients. This new survey, called the Localized Scleroderma Quality of Life Instrument (LoSQI), incorporated unique features associated with the disease, not captured by current surveys. As a secondary goal, the feasibility and usefulness of anchoring vignettes with pediatric patients were examined. The project included three phases; content domain development and item generation, a pilot study, and a field test. Validity evidence was gathered from multiple sources including test content, internal structure, and in relation to other variables. Overall, there was initial support for use of the LoSQI with pediatric LS patients. Patients indicated general understanding and readability of the items, and there was qualitative evidence for content validity. Exploratory factor analysis suggested the utility of reporting a total score along with two subscale scores, (1) Pain and Physical Functioning and (2) Body Image and Social Support. Reliability of both the subscale and total scores was acceptable. There was less evidence for use of anchoring vignettes in this context, as there was a high frequency of ties in rankings, which limited the utility of statistical models. Despite limitations from a small sample size and skewed response distributions, the pilot study and the field test provided promising initial evidence that the LoSQI can be used to capture HRQoL in LS patients ages 10-20 years. Future studies should examine responsiveness of the scores to change and optimal capture of HRQoL in patients <10 years of age

Additional file 10: of Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data

MetaboAnalyst 3.0 pathway results for each normalization method and both linear regression and mixture model analysis of phenotypic associations in HAPO Metabolomics. (DOCX 140 kb

Additional file 2: of Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data

R functions for mean centering, median scaling and Batch Normalizer that are not available in the R and Bioconductor packages used for the other normalization methods used in this study. (ZIP 12 kb

Additional file 4: Figures S1–S9. of Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data

Plots of RSD for simulated QC and analytical samples prior to and following normalization for randomly selected simulation rounds. Figure S1. RSD plots for simulation round 101. Figure S2. RSD plots for simulation round 115. Figure S3. RSD plots for simulation round 123. Figure S4. RSD plots for simulation round 190. Figure S5. RSD plots for simulation round 583. Figure S6. RSD plots for simulation round 732. Figure S7. RSD plots for simulation round 826. Figure S8. RSD plots for simulation round 866. Figure S9. RSD plots for simulation round 880. (PDF 14641 kb

Additional file 6: Figures S10–S16. of Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data

Plots of maternal and newborn QC HAPO Metabolomics samples prior to and following normalization for four selected metabolites. Figure S10. Plots of QC samples prior to and following mean centering. Figure S11. Plots of QC samples prior to and following median scaling. Figure S12. Plots of QC samples prior to and following quantile normalization. Figure S13. Plots of QC samples prior to and following quantile + ComBat. Figure S14. Plots of QC samples prior to and following EigenMS. Figure S15. Plots of QC samples prior to and following VSN. Figure S16. Plots of QC samples prior to and following Batch Normalizer. (PDF 774 kb

Additional file 10: of Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data

MetaboAnalyst 3.0 pathway results for each normalization method and both linear regression and mixture model analysis of phenotypic associations in HAPO Metabolomics. (DOCX 140 kb