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Item response models for longitudinal quality of life data in clinical trials
Author(s) -
Douglas Jeffrey A.
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19991115)18:21<2917::aid-sim204>3.0.co;2-n
Subject(s) - ordinal data , missing data , computer science , item response theory , quality of life (healthcare) , data quality , ordinal regression , clinical trial , scale (ratio) , covariate , quality (philosophy) , polytomous rasch model , fidelity , data mining , psychometrics , statistics , psychology , clinical psychology , machine learning , metric (unit) , medicine , mathematics , philosophy , operations management , physics , telecommunications , epistemology , pathology , quantum mechanics , economics , psychotherapist
Assessment of quality of life is becoming standard in clinical trials. A popular method for measuring quality of life is with instruments which utilize 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 clinical trials do not explicity consider the properties and psychometric features which were of interest in scale development. In this regard, the measurement and statistical summarization of quality of life data, along with the clinical interpretation, can be somewhat disjoint from the psychometric concerns of the development process. The aim of this paper is to address the complicated issues present in analysing multiple‐item ordinal quality of life data in clinical trials while maintaining fidelity to the psychometrical foundations upon which quality of life instruments are built. Accomplishing this will require the development of item response models which recognize the longitudinal aspects of clinical trial designs as well as the potential problem of informatively missing data. A general item response modeling approach is presented for longitudinal multiple‐item quality of life data measured on ordinal scales with model components for missing data mechanisms and latent trait regression on treatment indicators and other covariates. Copyright © 1999 John Wiley & Sons, Ltd.