Hierarchical finite mixture modelling of health outcomes: a risk-adjusted random effects approach [ 2003 - 2004 ]

Research Grant

Researchers: Prof Andy Lee (Principal investigator) ,  Kelvin Yau ,  Prof Geoffrey Mclachlan

Brief description In medical and health studies, finite mixture regression models have been used to analyze data arising from heterogeneous populations. Traditionally, the application of mixture models is mainly concerned with finite normal mixtures. Recent computational advances and methodological developments have enhanced the extension of the method to non-normal finite mixtures, such as the modelling of discrete responses in finite mixture of generalized linear models and overlapping phases of failure time data in the context of survival analysis. However, due to the hierarchical study design or the data collection procedure, the inherent correlation structure and-or clustering effects present may contribute to extra variations and violation of the independence assumption, resulting in spurious associations and misleading inferences based on the finite mixture model. This project aims to present a unified approach to accommodate both heterogeneity and dependency of observations, by incorporating random effects into finite mixture regression models. The new methodology will provide an integrated framework to analyze heterogeneous and correlated health outcomes. Three empirical studies are considered, namely, evaluation of an occupational injury reduction intervention, length of hospital stay modeling, and analysis of survival times of patients after cardiac surgery. The long term benefits to bioscience are accurate and valid conclusions inferred from medical and health studies, as well as the correct identification of high-risk subgroups. For the three application areas of this project, the improved analyses will specifically enable the evaluation of a participatory ergonomics intervention, the assessment of hospital efficiency and factors influencing length of hospitalization, and the determination of effectiveness of treatments prescribed pre- and post- operation, respectively.

Funding Amount $AUD 117,000.00

Funding Scheme NHMRC Project Grants

Notes Standard Project Grant