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Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

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Related Experiment Video

Updated: Mar 20, 2026

A Tactile Automated Passive-Finger Stimulator TAPS

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Unblinded by the Night: Predictive Power for Complex Bayesian Adaptive Trials When Sight Privileges Vary.

Byron J Gajewski¹, Jonathan Beall², Kaustubh Nimkar¹

¹Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.

Pharmaceutical Statistics

|March 18, 2026

Summary

This study introduces Bayesian predictive power to forecast clinical trial outcomes for blinded and unblinded teams. This method aids decision-making and resource allocation in complex adaptive trial designs.

Keywords:

DSMB predictive probabilities response adaptive randomization

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Area of Science:

Clinical Trials Methodology
Biostatistics
Adaptive Trial Design

Background:

Clinical trials require robust bias reduction strategies, including blinding investigators and sponsors.
Complex adaptive designs necessitate unblinded statisticians for interim analyses and oversight.
Data and Safety Monitoring Boards (DSMBs) require timely, accurate information for safety and efficacy monitoring.

Purpose of the Study:

To propose a novel method for predicting clinical trial outcomes using current data.
To enable blinded decision-making without compromising trial integrity.
To provide updated Bayesian predictive power for complex adaptive trial designs.

Main Methods:

Utilized Bayesian predictive power as a trial prediction method.
Applied the method to simulated data from the Hyperbaric Oxygen Brain Injury Treatment (HOBIT) trial.
Demonstrated updated Bayesian predictive power calculations for blinded and unblinded groups.

Main Results:

Bayesian predictive power effectively predicts trial outcomes and sample size distribution.
The approach facilitates resource allocation and decision-making for different trial teams.
Updated predictions offer valuable insights into future trial behavior.

Conclusions:

The proposed approach enhances guidance during clinical trial conduction.
Bayesian predictive power is applicable to various complex adaptive trial designs.
This method supports informed decision-making for both blinded and unblinded stakeholders.