Diagnostics for mixture regression models: applications to public health

Funding Activity

Website
http://purl.org/au-research/grants/nhmrc/425510

Funding Status
Closed

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Funded Activity Summary

In many public health studies, finite mixture regression models are often used to analyse data arising from heterogeneous populations. It is important to assess the stability of parameter estimates and the validity of statistical inferences when the underlying assumptions appear to be violated, but appropriate diagnostics are lacking in the literature. This research aims to develop effective diagnostic methods for assessing the adequacy of mixture regression models and the sensitivity of accompanying test statistics. The methodology developed will enable health care professionals to focus on substantive issues and to draw accurate and valid conclusions inferred from correlated and over-dispersed outcomes. In the presence of anomalous observations, the influence diagnostics can provide insights into the source of heterogeneity and the apparent over-dispersion, while accommodating the inherent correlation due to the longitudinal study design or nested data structure. Significance of the research lies in its scientific novelty and the breadth of its practical applications. The benefits to public health will accrue both nationally and internationally. For the empirical studies that motivated and are linked to this research, evaluation of health outcomes has significant implications in the prevention and control of recurrent urinary tract infections, hospital strategic planning, and post-stroke care and rehabilitation management. Moreover, appropriate assessment of a physical activity intervention for older adults is pertinent to falls prevention and reduction of musculoskeletal disorders among sedentary seniors.

Funded Activity Details

Start Date: 01-01-2007

End Date: 01-01-2008

Funding Scheme: NHMRC Project Grants

Funding Amount: $128,250.00

Funder: National Health and Medical Research Council

Research Topics

ANZSRC Field of Research (FoR)

Applied Statistics

ANZSRC Socio-Economic Objective (SEO)

There are no SEO codes available for this funding activity

Other Keywords

biostatistical methods | health outcomes | information | intersubject variation | longitudinal | multivariate statistics | patient related health outcomes

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