The use of different statistical approaches in examining the longitudinal change in quality of life

Abstract

Quality of life (QoL) is now firmly recognized as a significant outcome measure in

public health, clinical and patient care research (1, 2). Despite a growing trend in

conducting longitudinal QoL studies, the longitudinal changes in QoL in the general

population remain poorly understood due to the limited number of studies.

Furthermore, few studies have discussed the use of different statistical methods in

analyzing the longitudinal change in QoL. This paper aimed to discuss the

application of traditional statistical approach: R-ANOVA and newer statistical

approaches: LMM and LGCA in analyzing the longitudinal change in QoL. The

underlying assumptions, characteristics and specifications of each of the statistical

methods were explained. Different public health studies that examined the

longitudinal change of QoL would be elaborated in order to show how the criterions

of each statistical method were fulfilled in the research analysis. Additionally, the

limitations of applying the traditional statistical approach: R-ANOVA and the newer

statistical approaches: LMM and LGCA in analyzing longitudinal QoL data will be

discussed with the emphasis on how each analytical method overcome the

weaknesses of one another. The understanding of the application of different

statistical approaches in analyzing the longitudinal change in QoL can advance the

future development of a robust statistical approach for QoL research.