Modelling Severe Asthma Variation
thesisposted on 06.02.2013 by Christopher James Newby
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Asthma is a heterogeneity disease that is mostly managed successfully using bronchodilators and anti-inflammatory drugs. Around 10%-15% of asthmatics however have difficult or severe asthma which is less responsive to treatments. Asthma and in particular severe asthma are now thought of a description of symptoms which may contain possible sub-groups with possible different pathologies which could be useful for targeting different drugs for different sub-groups. However little statistical work has been carried out to determine these sub-phenotypes. Studies have been carried out to partition severe asthma variables in to a number of sub-groups but the algorithms used in these studies are not based on statistical inference and it is difficult to select the number of best fitting sub-groups using such methods. It is also unclear where the clusters or sub-groups returned are actual sub-groups or reflect a bigger non-normal distribution. In the thesis we have developed a statistical model that combines factor analysis, a method used to obtain independent factors to describe processes allowing for variation over variables, and infinite mixture modelling, a process that involves determining the most probable number of mixtures or clusters thus allowing for variation over individuals. This model created is a Dirichlet process normal mixture latent variable model DPNMLVN and it is capable of determining the correct number of mixtures over each factor. The model was tested with simulations and used to analysis two severe asthma datasets and a cancer clinical trial. Sub-groups were found that reflect a high Eosinophilic group and an average eosinophilic group, a late onset older non atopic group and a highly atopic younger early onset group. In the clinical trial data 3 distinct mixtures were found relating to existing biomarkers not used in the mixture analysis.