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Through Alzheimer's Disease Neuroimaging Initiative (ADNI), time-to-eventdata: from the pre-dementia state of mild cognitive impairment (MCI) to thediagnosis of Alzheimer's disease (AD), is collected and analyzed by explicitlyunraveling prognostic heterogeneity among 346 uncensored and 557 right censoredsubjects under structural dependency among covariate features. Thenon-informative censoring mechanism is tested and confirmed based onconditional-vs-marginal entropies evaluated upon contingency tables built bythe Redistribute-to-the-right algorithm. The Categorical Exploratory DataAnalysis (CEDA) paradigm is applied to evaluate conditional entropy-basedassociative patterns between the categorized response variable against 16categorized covariable variables all having 4 categories. Two order-1 globalmajor factors: V9 (MEM-mean) and V8 (ADAS13.bl) are selected sharing thehighest amounts of mutual information with the response variable. This heavilycensored data set is analyzed by Cox's proportional hazard (PH) modeling.Comparisons of PH and CEDA results on a global scale are complicated under thestructural dependency of covariate features. To alleviate such complications,V9 and V8 are taken as two potential perspectives of heterogeneity and theentire collections of subjects are divided into two sets of foursub-collections. CEDA major factor selection protocol is applied to allsub-collections to figure out which features provide extra information. Graphicdisplays are developed to explicitly unravel conditional entropy expansionsupon perspectives of heterogeneity in ADNI data. On the local scale, PHanalysis is carried out and results are compared with CEDA's. We conclude that,when facing structural dependency among covariates and heterogeneity in data,CEDA and its major factor selection provide significant merits for manifestingdata's multiscale information content.

Unraveling heterogeneity of ADNI's time-to-event data using conditional entropy Part-I: Cross-sectional study