Item response models for longitudinal quality of life data in clinical trials

Assessment of quality of life is becoming standard in clinical trials. A po pular method for measuring quality of life is with instruments which utiliz e multiple-item subscales, in which each item is scored on a Likert scale. Most statistical methods for the analysis of quality of life data in clinic al trials do not explicity consider the properties and psychometric feature s which were of interest in scale development. In this regard, the measurem ent and statistical summarization of quality of life data, along with the c linical interpretation, can be somewhat disjoint from the psychometric conc erns of the development process. The aim of this paper is to address the co mplicated issues present in analysing multiple-item ordinal quality of life data in clinical trials while maintaining fidelity to the psychometrical f oundations upon which quality of life instruments are built. Accomplishing this will require the development of item response models which recognize t he longitudinal aspects of clinical trial designs as well as the potential problem of informatively missing data. A general item response modeling app roach is presented for longitudinal multiple-item quality of life data meas ured on ordinal scales with model components for missing data mechanisms an d latent trait regression on treatment indicators and other covariates. Cop yright (C) 1999 John Wiley & Sons, Ltd.