Abstract
Item Response Theory (IRT) is a promising concept for constructing and calibrating health related quality of life questionnaires. The most popular IRT model is the two-parameter logistic model, and its parameters are often estimated by the Marginal Maximum Likelihood method (MML). However, performance of estimation by MML is poor in a small sample, such as severe or rare disease patients. The purpose of this study was to propose a Bayesian method for improving the performance and to compare the precision and accuracy of estimates by different methods with a simulation. In the simulation, item responses were generated according to the two-parameter logistic model. The estimates obtained using a MML Program (MULTILOG) and a Bayesian program written in this study were compared with respect to the precision and accuracy. The proposed method could improve the performance about difficulty parameters using more than 100 individuals and 10 items. Though that about discrimination parameters was still poor. For example, 137 patients received the Parkinson's disease specific questionnaire (PDQ-39) and a generic one (SF-36); and we calibrated items of physical functioning in these questionnaire simultaneously. It was shown that half of the PDQ-39 items could measure the ability of patients at a little bit high level of physical functioning more efficiently than SF-36.
