Equipoise calibration of clinical trial design

TL;DR

This paper introduces a method to calibrate clinical trial designs to ensure that statistical significance aligns with clinical equipoise, enhancing the reliability of trial outcomes.

Contribution

It provides a formal calibration framework linking statistical properties of trial outcomes to clinical equipoise imbalance, applicable to phase 2 and phase 3 oncology studies.

Findings

Common trial designs achieve at least 90% evidence of equipoise imbalance.

Power and error rates correlate with strong equipoise imbalance.

Large sample sizes are needed to establish imbalance with inconsistent phase 2 and 3 outcomes.

Abstract

Clinical trial design ensures that primary analysis outcomes have strong statistical properties. However, mainstream methodology for randomised study design does not establish a formal link between statistical and clinical significance. This paper contributes to bridging this gap by calibrating the operational characteristics of primary trial outcomes to establishing clinical equipoise imbalance. Common late phase designs are shown to provide at least 90% evidence of equipoise imbalance. Designs carrying 95% power at 5% false positive rate are shown to demonstrate 95% evidence of equipoise imbalance, providing an operational definition of a robustly powered study. Equipoise calibration is applied to design of clinical development plans comprising phase 2 and phase 3 studies using standard oncology endpoints. Commonly used power and false positive error rates are shown to provide strong equipoise imbalance when positive outcomes are observed in both phase 2 and phase 3. Establishing strong equipoise imbalance based on inconsistent outcomes of phase 2 and phase 3 studies is shown to require large sample sizes unlikely to be associated with clinically meaningful effect sizes.