Efficacy Analysis in Clinical Trials: A Comprehensive Review of Statistical and Machine Learning Approaches

TL;DR

This review comprehensively discusses traditional and modern statistical and machine learning methods used to assess efficacy in clinical trials, highlighting their applications, strengths, limitations, and future challenges.

Contribution

It provides an integrated overview of efficacy testing approaches, bridging classical statistics with emerging machine learning techniques in clinical trial analysis.

Findings

Parametric methods are efficient under well-defined assumptions.

Nonparametric techniques are robust for non-normal data.

Machine learning approaches are transforming trial design and outcome prediction.

Abstract

Efficacy testing is a cornerstone of clinical trials, ensuring that medical interventions achieve their intended therapeutic effects. Over the decades, a wide range of statistical methodologies have been developed to address the complexities of clinical trial data, including parametric, nonparametric, Bayesian, and machine learning approaches. Parametric methods, such as t-tests, ANOVA, and LMMs, have traditionally been the foundation of efficacy testing due to their efficiency under well-defined assumptions. Nonparametric techniques, including the Friedman test, Brunner-Munzel test, and modern extensions like nparLD, have emerged as robust alternatives, particularly for skewed, ordinal, or non-normal data. Bayesian methodologies have enabled the incorporation of prior information and uncertainty quantification, while machine learning techniques, such as deep learning and reinforcement learning, are revolutionizing trial designs and outcome predictions. Despite these advancements, significant gaps remain, including challenges in handling high-dimensional data, missingness, and ensuring equitable efficacy testing across diverse populations. This review provides a comprehensive overview of these statistical methods, highlighting their applications, strengths, limitations, and future directions. By bridging traditional statistical frameworks with modern computational techniques, the field can continue to advance toward more reliable and personalized clinical trial methodologies.