Communicating results in trials with multiple hypotheses or adaptive design features

2026-05-05

AI Summary

This study addresses the underappreciated challenge of estimation and communication following multiplicity adjustment within the frequentist framework in complex clinical trials, where multiple endpoints, interim data looks, or group comparisons often introduce estimation bias and complicate interpretation, thereby undermining transparency in benefit–risk assessment. By integrating advanced methodologies such as adaptive designs and graphical approaches to multiple testing, the work illustrates through concrete examples the limitations of current strategies in conveying trial results meaningfully. The research underscores the need to critically reevaluate prevailing practices and foster interdisciplinary dialogue to enhance both the accuracy of effect estimation and the clarity of result communication, ultimately informing future methodological standards and regulatory guidance.

Abstract

Over time, clinical trials have increasingly incorporated complex design and analysis elements such as interim analyses, adaptations, multiple endpoints, and sophisticated multiplicity schemes for multiple endpoints and/or treatment arms following the paradigm of frequentist inference. In frequentist clinical trials multiplicity can come from (at least) four sources: multiple looks at the data, multiple endpoints, multiple populations, or multiple treatment comparisons. Normally, Type 1 error control across the multiple hypotheses is implemented to control chance of false positive decisions. To achieve this advanced techniques such as adaptive designs or graphical multiple testing procedures have been developed and are used in the design of clinical trials. However, these methods focus on hypothesis testing while subsequent estimation remains crucial to allow for a benefit-risk assessment and further use of the results by various stakeholders. Through examples, we illustrate challenges in estimation and transparent communication. In general, there are no simple solutions to this conceptual and communicational challenge. The purpose of this paper is to generate awareness of these issues and initiate a discussion about how to address them moving forward.

Problem

• multiple hypotheses
• adaptive design
• Type I error control
• estimation
• transparent communication

Innovation

• adaptive designs
• multiple testing
• Type I error control
• estimation after selection
• transparent communication