Haley Kate Robinson | Searcharxiv

arXiv2026

Modeling Age-Adjusted Mortality in the United States

This research explores how total mortality figures relate to age-standardized death rates within the United States, using the complete historical record of national mortality statistics. Through a detailed investigation of both all-cause and cause-specific mortality trends, the study evaluates the impact of demographic standardization on interpreting mortality data across different time periods and geographic regions. Results indicate a robust and persistent association between crude death totals and age-adjusted rates. However, the findings also demonstrate that without adjusting for age, comparisons over time or across locations may misrepresent underlying epidemiological shifts, largely due to evolving population age structures. The study underscores the critical role of age adjustment as a methodological tool for generating accurate, interpretable, and comparable measures of public health outcomes.

q-bio.OT↗

arXiv2026

A Bayesian Discrete Framework for Enhancing Decision-Making Processes in Clinical Trial Designs and Evaluations

This study examines the application of Bayesian approach in the context of clinical trials, emphasizing their increasing importance in contemporary biomedical research. While conventional frequentist approach provides a foundational basis for analysis, it often lacks the flexibility to integrate prior knowledge, which can constrain its effectiveness in adaptive settings. In contrast, Bayesian methods enable continual refinement of statistical inferences through the assimilation of accumulating evidence, thereby supporting more informed decision-making and improving the reliability of trial findings. This paper also considers persistent challenges in clinical investigations, including replication difficulties and the misinterpretation of statistical results, suggesting that Bayesian strategies may offer a path toward enhanced analytical robustness. Moreover, discrete probability models, specifically the Binomial, Poisson, and Negative Binomial distributions are explored for their suitability in modeling clinical endpoints, particularly in trials involving binary responses or data with overdispersion. The discussion further incorporates Bayesian networks and Bayesian estimation techniques, with a comparative evaluation against maximum likelihood estimation to elucidate differences in inferential behavior and practical implementation.

stat.ME↗