Barons, Martine J. (2013) What is the added value of using non-linear models to explore complex healthcare datasets? PhD thesis, University of Warwick.

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Abstract

Health care is a complex system and it is therefore expected to behave in a non-linear
manner. It is important for the delivery of health interventions to patients that the
best possible analysis of available data is undertaken. Many of the conventional
models used for health care data are linear. This research compares the performance
of linear models with non-linear models for two health care data sets of complex
interventions.
Logistic regression, latent class analysis and a classification artificial neural network
were each used to model outcomes for patients using data from a randomised controlled
trial of a cognitive behavioural complex intervention for non-specific low back
pain. A Cox proportional hazards model and an artificial neural network were used
to model survival and the hazards for different sub-groups of patients using an observational
study of a cardiovascular rehabilitation complex intervention.
The artificial neural network and an ordinary logistic regression were more accurate
in classifying patient recovery from back pain than a logistic regression on latent
class membership. The most sensitive models were the artificial neural network and
the latent class logistic regression. The best overall performance was the artificial
neural network, providing both sensitivity and accuracy.
Survival was modelled equally well by the Cox model and the artificial neural network,
when compared to the empirical Kaplan-Meier survival curve. Long term
survival for the cardiovascular patients was strongly associated with secondary prevention
medications, and fitness was also important. Moreover, improvement in
fitness during the rehabilitation period to a fairly modest 'high fitness' category was
as advantageous for long-term survival as having achieved that same level of fitness
by the beginning of the rehabilitation period. Having adjusted for fitness, BMI was
not a predictor of long term survival after a cardiac event or procedure.
The Cox proportional hazards model was constrained by its assumptions to produce
hazard trajectories proportional to the baseline hazard. The artificial neural network
model produced hazard trajectories that vary, giving rise to hypotheses about how
the predictors of survival interact in their influence on the hazard.
The artificial neural network, an exemplar non-linear model, has been shown to
match or exceed the capability of conventional models in the analysis of complex
health care data sets.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics R Medicine > R Medicine (General)
Library of Congress Subject Headings (LCSH):	Medical care -- Research -- Statistical methods, Big data, Nonlinear theories, Regression analysis, Neural networks (Computer science) -- Statistical methods
Official Date:	October 2013
Institution:	University of Warwick
Theses Department:	Centre for Complexity Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Griffiths, Frances; Parsons, Nicholas R.; Thorogood, Margaret
Sponsors:	Engineering and Physical Sciences Research Council (EPSRC); University of Warwick
Extent:	viii, 1, 266 leaves : illustrations.
Language:	eng

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