Ordinal regression model and the linear regression model were superior to the logistic regression models

J Clin Epidemiol. 2006 May;59(5):448-56. doi: 10.1016/j.jclinepi.2005.09.007. Epub 2006 Mar 14.


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.

Publication types

  • Comparative Study

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Aged
  • Cardiac Catheterization
  • Cohort Studies
  • Coronary Disease / therapy*
  • Female
  • Humans
  • Linear Models
  • Logistic Models
  • Male
  • Middle Aged
  • Quality of Life*
  • Regression Analysis*
  • Sex Distribution
  • Treatment Outcome