OBJECTIVE: Ordinal scales often generate scores with skewed data distributions. The optimal method of analyzing such data is not entirely clear. The objective was to compare four statistical multivariable strategies for analyzing skewed health-related quality of life (HRQOL) outcome data. HRQOL data were collected at 1 year following catheterization using the Seattle Angina Questionnaire (SAQ), a disease-specific quality of life and symptom rating scale. STUDY DESIGN AND SETTING: In this methodological study, four regression models were constructed. The first model used linear regression. The second and third models used logistic regression with two different cutpoints and the fourth model used ordinal regression. To compare the results of these four models, odds ratios, 95% confidence intervals, and 95% confidence interval widths (i.e., ratios of upper to lower confidence interval endpoints) were assessed. RESULTS: Relative to the two logistic regression analysis, the linear regression model and the ordinal regression model produced more stable parameter estimates with smaller confidence interval widths. CONCLUSION: A combination of analysis results from both of these models (adjusted SAQ scores and odds ratios) provides the most comprehensive interpretation of the data.
OBJECTIVE: Ordinal scales often generate scores with skewed data distributions. The optimal method of analyzing such data is not entirely clear. The objective was to compare four statistical multivariable strategies for analyzing skewed health-related quality of life (HRQOL) outcome data. HRQOL data were collected at 1 year following catheterization using the Seattle Angina Questionnaire (SAQ), a disease-specific quality of life and symptom rating scale. STUDY DESIGN AND SETTING: In this methodological study, four regression models were constructed. The first model used linear regression. The second and third models used logistic regression with two different cutpoints and the fourth model used ordinal regression. To compare the results of these four models, odds ratios, 95% confidence intervals, and 95% confidence interval widths (i.e., ratios of upper to lower confidence interval endpoints) were assessed. RESULTS: Relative to the two logistic regression analysis, the linear regression model and the ordinal regression model produced more stable parameter estimates with smaller confidence interval widths. CONCLUSION: A combination of analysis results from both of these models (adjusted SAQ scores and odds ratios) provides the most comprehensive interpretation of the data.
Authors: Helen Razmjou; Joel A Finkelstein; Albert Yee; Richard Holtby; Marjan Vidmar; Michael Ford Journal: Physiother Can Date: 2009-02-13 Impact factor: 1.037
Authors: Jian Li; Marc N Jarczok; Adrian Loerbroks; Ina Schöllgen; Johannes Siegrist; Jos A Bosch; Mark G Wilson; Daniel Mauss; Joachim E Fischer Journal: Int J Behav Med Date: 2013-12
Authors: Kay H Stricker; Oliver Kimberger; Kurt Schmidlin; Marcel Zwahlen; Ulrike Mohr; Hans U Rothen Journal: Intensive Care Med Date: 2009-12 Impact factor: 17.440
Authors: Kimberly R Boer; Oddeke van Ruler; Arnold A P van Emmerik; Mirjam A Sprangers; Sophia E de Rooij; Margreeth B Vroom; Corianne A J M de Borgie; Marja A Boermeester; Johannes B Reitsma Journal: Intensive Care Med Date: 2008-01-16 Impact factor: 17.440