A review of ordinal regression models applied on health-related quality of life assessments.

There has been increasing emphasis in medical research on the design and analysis of quality of life scales. Many quality of life scales are ordinal and statistical methods such as ordinal regression models have been reviewed on a number of occasions. However, when such models are applied, the way the data have been generated is often overlooked. In this paper we illustrate the use of ordinal regression models, in particular the proportional odds model, the partial proportional odds model and the stereotype model in the MRC Cognitive Function and Ageing Study (MRC CFAS). The partial proportional odds and the stereotype models are often under-utilized, perhaps due to the lack of available software. However, in this paper, analysis based on these models has been carried out using the popular statistical software package SAS and macros devised in SAS. Furthermore, bootstrapping techniques have been applied to obtain valid estimates of the standard errors of the parameters in the stereotype model. Strikingly different results were obtained using the different ordinal regression models. We conclude that the way the data have been generated is particularly important for the analysis of quality of life assessments. Different methods of generating scores yield data with different properties. It is now possible to fit a variety of ordinal regression models and so select the appropriate one that correctly models the data.