[Problems raised by interim analysis in therapeutic trials]

Area of Science:

• Clinical Trials
• Biostatistics
• Statistical Methods

Context:

• Interim analyses are crucial for early decision-making in clinical trials.
• Performing analyses before full data collection increases the risk of erroneous findings.
• Repeated statistical testing inflates the overall significance level, potentially leading to false positives.

Purpose:

• To highlight the risks associated with interim analyses in clinical research.
• To introduce statistical methods designed for repeated significance testing.
• To provide an overview of Classical Sequential Analysis and Group Sequential Analysis.

Summary:

• Interim analyses, conducted before all study data are available, pose a significant risk of false conclusions.
• The frequency of these analyses inflates the Type I error rate; for example, ten tests at alpha=5% nominal level increase the overall significance to 19%.
• This document describes statistical approaches such as Armitage's Classical Sequential Analysis and Pocock's Group Sequential Analysis to mitigate these risks.

Impact:

• Enhances understanding of statistical challenges in adaptive clinical trial designs.
• Promotes more rigorous application of statistical methods in ongoing research.
• Aims to improve the reliability of clinical trial results by addressing the issue of repeated testing.