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. / published_or_final_version / Community Medicine / Master / Master of Public Health
Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/174450 |
Date | January 2012 |
Creators | 王曉暉, Wong, Hiu-fai, Jennifer. |
Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
Source Sets | Hong Kong University Theses |
Language | English |
Detected Language | English |
Type | PG_Thesis |
Source | http://hub.hku.hk/bib/B47657558 |
Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
Relation | HKU Theses Online (HKUTO) |
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