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Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Related Experiment Video

Updated: Jul 6, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

Stratified and randomized play-the-winner rule.

Yuanyuan Liang¹, Kimmie Chough Carriere

¹Alberta Research Centre for Child Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada.

Statistical Methods in Medical Research

|April 1, 2008

Summary

This summary is machine-generated.

A new stratified and randomized play-the-winner rule (SRPWR) improves treatment assignment in sequential clinical trials. This method balances patient allocation to effective treatments while accounting for confounders and delayed responses.

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Area of Science:

Clinical Trials Methodology
Biostatistics
Medical Research Design

Background:

Sequential clinical trials require efficient treatment allocation rules.
Existing methods like the randomized play-the-winner rule have limitations with multiple treatments and confounders.

Purpose of the Study:

To introduce a novel allocation rule, the stratified and randomized play-the-winner rule (SRPWR).
To extend the play-the-winner concept to trials with more than two treatments.
To address treatment success probabilities influenced by confounders and delayed responses.

Main Methods:

Proposed the stratified and randomized play-the-winner rule (SRPWR).
Developed the rule for sequential trials with multiple treatment arms.
Incorporated handling of known patient confounders (e.g., age, gender, disease status).
Accounted for treatments with delayed response probabilities.

Main Results:

The SRPWR demonstrates an average assignment of more patients to the superior treatment.
The proposed rule effectively eliminates selection bias in treatment allocation.
The method successfully accommodates delayed treatment response data.

Conclusions:

The SRPWR offers an advanced approach for treatment assignment in complex sequential trials.
This rule enhances trial efficiency and reduces bias, particularly with multiple treatments and confounders.
SRPWR is suitable for clinical settings with delayed treatment efficacy.