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

TL;DR

This study analyzes ADNI's time-to-event data for Alzheimer's progression, revealing heterogeneity and covariate dependencies using entropy-based methods and Cox modeling, to improve understanding of prognostic factors.

Contribution

It introduces a conditional entropy-based framework combined with Cox modeling to analyze heterogeneity and covariate dependencies in censored Alzheimer's data.

Findings

V9 and V8 are key factors with high mutual information.

CEDA reveals associative patterns among covariates.

Heterogeneity impacts the interpretation of survival analysis.

Abstract

Through Alzheimer's Disease Neuroimaging Initiative (ADNI), time-to-event data: from the pre-dementia state of mild cognitive impairment (MCI) to the diagnosis of Alzheimer's disease (AD), is collected and analyzed by explicitly unraveling prognostic heterogeneity among 346 uncensored and 557 right censored subjects under structural dependency among covariate features. The non-informative censoring mechanism is tested and confirmed based on conditional-vs-marginal entropies evaluated upon contingency tables built by the Redistribute-to-the-right algorithm. The Categorical Exploratory Data Analysis (CEDA) paradigm is applied to evaluate conditional entropy-based associative patterns between the categorized response variable against 16 categorized covariable variables all having 4 categories. Two order-1 global major factors: V9 (MEM-mean) and V8 (ADAS13.bl) are selected sharing the highest amounts of mutual information with the response variable. This heavily censored data set is analyzed by Cox's proportional hazard (PH) modeling. Comparisons of PH and CEDA results on a global scale are complicated under the structural dependency of covariate features. To alleviate such complications, V9 and V8 are taken as two potential perspectives of heterogeneity and the entire collections of subjects are divided into two sets of four sub-collections. CEDA major factor selection protocol is applied to all sub-collections to figure out which features provide extra information. Graphic displays are developed to explicitly unravel conditional entropy expansions upon perspectives of heterogeneity in ADNI data. On the local scale, PH analysis 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 manifesting data's multiscale information content.