Linear structural equation model in analyzing quality-of-life-data from clinical trials

Abstract

Assessment of quality of life (QOL) in clinical trials becomes a challenging task from the viewpoint of clinical biostatistics. The responses of the items for measuring QOL indices usually vary widely from patient to patient and from time to time. Measurement errors might be present in the responses of the items, and they might be correlated. Hence, in analyzing QOL data, the usual assumption that there are no measurement errors in responses is too liberal. Because the QOL indices are likely to be correlated, separate analysis of each index might not be efficient from the point of view of statistical methodology. We apply linear structural equation modeling (LISREL) in assessing the QOL data obtained from a clinical trial. A basic premise of the LISREL approach is that the abstract concepts (latent constructs) that are not directly measurable can be studied. LISREL is a statistical procedure for conceiving and testing structural hypotheses that cannot be tested adequately with other statistical procedures. It allows us to specify relations between unobserved and observed variables while controlling for measurement errors and correlations among both the measurement errors and the latent constructs.