Mathematical Analysis of Regression Model Epidemiology

Abstract

Statistical modeling techniques, specifically regression line analysis have become important analytical tools and are contributing immensely to the field of epidemiology. However, many users do not understand their effective use and applications. Underlying epidemiological concepts and not the statistics should govern or justify the proper use and application of any modeling exercise. Main utility of the regression line analysis lies in its ability to provide a general but practical conceptual framework for casual problems, explaining and evaluating the role of biases, confounders and effect modifiers. Successful modeling of complex data is a part science, part statistics and part experience, but the major part is logic or common sense. Findings of this research article focuses on the contributions of regression analysis towards the pedagogical study of epidemiological models by enhancing the research process and serving as an effective tool for communicating findings to public health managers and policymakers and fostering interdisciplinary collaboration.