A Statistical and Epidemiological Framework for Unraveling the Etiology of Cryptogenic Cirrhosis
DOI:
https://doi.org/10.59652/ahnthv08Keywords:
NASH, Cryptogenic cirrhosis, liver damage, liver function parameters, inhalation exposure, modelling, machine learning, Statistics, hepatologyAbstract
Cryptogenic cirrhosis is a diagnosis of exclusion with heterogeneous and often overlapping etiologies, making systematic investigation challenging in routine clinical practice. We propose a unified statistical and epidemiological framework that integrates classical study designs (case–control and cohort with survival analysis), evidence synthesis (meta-analysis), exploratory genetics (genome-wide association studies), and modern machine learning to examine potential contributors such as long-term analgesic exposure and metabolic comorbidities. A motivating clinical vignette is used solely to illustrate the clinical context in which such analyses may arise. All quantitative results presented in this study are derived from fully simulated datasets constructed to demonstrate the behavior and interpretability of the proposed workflow rather than to establish real-world causal effects. Within these illustrative simulations, analgesic exposure is specified to act as a risk factor, leading to elevated association measures in case–control analyses, separation of survival curves in cohort analyses, and high feature importance in predictive models, while exploratory GWAS simulations yield no genome-wide significant signals, underscoring the need for adequately powered real studies. The proposed workflow is transparent, reproducible, and deployable in prospective registries, with the primary goal of generating testable etiologic hypotheses rather than confirming definitive clinical associations.
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