Helen M McTaggart-Cowan1, John E Brazier, Aki Tsuchiya. 1. School ofHealth and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK. h.m.cowan@sheffield.ac.uk
Abstract
PURPOSE: Health states that describe an investigated condition are a crucial component of valuation studies. The health states need to be distinct and comprehensible to those who appraise them. The objective of this study was to describe a novel application of Rasch and cluster analyses in the development of three rheumatoid arthritis health states. METHODS: The Stanford Health Assessment Questionnaire (HAQ) was subjected to Rasch analysis to select the items that best represent disability. K-means cluster analysis produced health states with the levels of the selected items. The pain and discomfort dimension from the EuroQol-5D was also incorporated. RESULTS: The results demonstrate a methodology for reducing a dataset containing individual disease-specific scores to generate health states. The four selected HAQ items were bending down, climbing steps, lifting a cup to your mouth, and standing up from a chair. CONCLUSIONS: The combined use of Rasch and k-means cluster analysis has proved to be an effective technique for identifying the most important items and levels for the construction of health states.
PURPOSE: Health states that describe an investigated condition are a crucial component of valuation studies. The health states need to be distinct and comprehensible to those who appraise them. The objective of this study was to describe a novel application of Rasch and cluster analyses in the development of three rheumatoid arthritis health states. METHODS: The Stanford Health Assessment Questionnaire (HAQ) was subjected to Rasch analysis to select the items that best represent disability. K-means cluster analysis produced health states with the levels of the selected items. The pain and discomfort dimension from the EuroQol-5D was also incorporated. RESULTS: The results demonstrate a methodology for reducing a dataset containing individual disease-specific scores to generate health states. The four selected HAQ items were bending down, climbing steps, lifting a cup to your mouth, and standing up from a chair. CONCLUSIONS: The combined use of Rasch and k-means cluster analysis has proved to be an effective technique for identifying the most important items and levels for the construction of health states.