COMPARATIVE ANALYSIS OF STATISTICAL METHODS IN CLINICAL TRIALS

Abstract

This study explores the methodological foundations of statistical evaluation in the context of clinical trials, with a focus on enhancing the precision and credibility of conclusions derived from empirical data. It delves into the theoretical and applied dimensions of key statistical techniques—including, but not limited to, analysis of variance and regression-based inference models—as instruments for verifying therapeutic effectiveness and identifying potential risk factors. The discussion expands on how the structure of collected data, the nature of the clinical design, and the sequential stage of pharmaceutical testing condition the appropriateness and interpretive power of each method. Particular emphasis is placed on the comparative performance of these analytical tools in managing inferential uncertainty, specifically with respect to minimizing the probability of Type I and Type II statistical errors. Through selected case-based illustrations, the paper demonstrates that the rigor and contextual suitability of statistical method selection have profound implications for both research integrity and the validity of clinical recommendations. Ultimately, the study advocates for a more nuanced integration of advanced statistical thinking into the decisionmaking framework of contemporary medical practice