A Generalizable Distribution Structure Analysis Algorithm with Audit-Ready Framework for Medical Research
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Background
Conventional statistical methods in medical research often fail to capture real-world complexity due to rigid parametric assumptions, particularly normality, which frequently do not hold for clinical and epidemiological data. Heterogeneous distributions, heavy-tailed patterns, and multimodal structures are common in healthcare data, yet conventional methods often fail to capture these structural characteristics, leading to information loss and potentially misleading conclusions. Furthermore, regulatory audits and reproducibility requirements demand transparent, traceable analytical frameworks.
Objective
This study presents a comprehensive Distribution Structure Analysis (DSA) algorithm with an integrated audit-ready framework designed specifically for medical research. The algorithm systematically identifies distributional structures, ensures statistical rigor through explicit estimand specification and goodness-of-fit testing, and maintains complete audit trails for regulatory compliance.
Methods
The DSA algorithm integrates five key components: (1) explicit estimand specification aligned with research design, (2) automated distribution type identification (normal, log-normal, exponential, Weibull, power-law, and mixture models), (3) comprehensive goodness-of-fit assessment using multiple criteria (AIC/BIC, visual diagnostics, and statistical tests), (4) causal inference support through Directed Acyclic Graphs (DAG), and (5) automated audit logging with a three-tier quality control system (red/yellow/green). The algorithm was validated using both simulated datasets with known distributions and real-world medical data from clinical trials and epidemiological studies.
Results
Validation studies demonstrated that the DSA algorithm correctly identified distribution types with 95% accuracy across 1,000 simulated datasets. In clinical trial data analysis, the algorithm detected heavy-tailed distributions in adverse event frequencies that were missed by conventional normality-based methods, leading to more accurate safety assessments. The audit logging system successfully recorded all analytical decisions, enabling complete reproducibility. The three-tier quality control system flagged 12% of analyses for re-examination, preventing potential methodological errors. Application to epidemiological data revealed multimodal patterns in disease incidence that informed targeted public health interventions.
Conclusions
The DSA algorithm with integrated audit-ready framework provides a rigorous, transparent, and reproducible approach to distribution structure analysis in medical research. By explicitly addressing estimands, ensuring goodness-of-fit, and maintaining complete audit trails, the framework meets both statistical rigor and regulatory compliance requirements. The algorithm is applicable across diverse medical research domains, including clinical trials, epidemiology, health economics, and pharmacovigilance. Open-source implementation and comprehensive documentation facilitate adoption and validation by the research community.
