Document Type

Article

Publication Date

1-2026

Publisher

Wolters Kluwer/Aspen Publishers

Source Publication

Journal of Contemporary Behavioural and Social Research

Source ISSN

1085-4568

Abstract

Objective: This study aims to bridge the gap between statistical significance and clinical relevance using rigorous statistical methodologies applied to simulated healthcare data, thereby enhancing the translational value of research outcomes for patient‑centered care. Methods: A synthetic dataset representing 10,000 patients was generated, and descriptive statistics were applied to compare two treatment groups. Binary outcomes were predicted using both a logistic regression model and a neural network. Two‑tailed tests with a significant level of 0.05 were employed, utilizing SAS version 9.4 and IBM SPSS Statistics, Version 29.0 (IBM Corp., Armonk, New York, United States of America). Results: The simulated dataset reflected a realistic patient population with balanced demographic and clinical characteristics across treatment groups. Logistic regression identified gender, cardiovascular disease, and diabetes as significant predictors of adverse outcomes, with a moderate predictive accuracy (c‑statistic = 0.62). Neural network analysis confirmed these patterns, highlighting consistent risk factors across methods. Importantly, while some treatment effects reached statistical significance, their clinical impact was negligible, emphasizing the distinction between numerical significance and meaningful patient outcomes. Conclusion: The efficacy of simulated datasets in replicating authentic healthcare scenarios is underscored by our findings, providing insights into the impacts of various treatments on patient outcomes. The analytical models employed highlight the significance of integrating statistical rigor with clinical insight, thereby reinforcing this approach’s value in healthcare research and its potential to inform clinical decision‑making.

Comments

Published version. Journal of Contemporary Behavioural and Social Research, Vol. 1, No. 2 (2025): 79-84. DOI. This article is © 2026 Wolters Kluwer – Medknow. Used with permission.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License (CC BY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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