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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

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

Cluster randomized trials with treatment noncompliance.

Booil Jo¹, Tihomir Asparouhov, Bengt O Muthén

¹Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. booil@stanford.edu

Psychological Methods

|March 12, 2008

Summary

Cluster randomized trials (CRTs) with noncompliance require simultaneous analysis. This study shows how to account for both clustering and nonadherence for valid intervention effect estimation.

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Area of Science:

Statistics
Epidemiology
Biostatistics

Background:

Cluster randomized trials (CRTs) are common in field experiments.
Noncompliance (nonadherence) is a frequent issue in CRTs.
Clustering and noncompliance are often analyzed separately, potentially leading to invalid results.

Purpose of the Study:

To investigate the impact of simultaneous clustering and noncompliance on statistical inferences in CRTs.
To propose and evaluate methods for accounting for both complications concurrently.
To estimate the complier average causal effect in the presence of clustering and noncompliance.

Main Methods:

Monte Carlo simulations were used to assess the effects of clustering and noncompliance.
An ML-EM (Maximum Likelihood - Expectation-Maximization) estimation method was employed.
The focus was on estimating the intervention effect among compliers.

Main Results:

Ignoring the interplay between clustering and noncompliance can lead to biased statistical inferences.
Simultaneous analysis of clustering and noncompliance is crucial for accurate results.
The ML-EM method effectively estimates intervention effects under these conditions.

Conclusions:

CRTs with noncompliance necessitate methods that address both clustering and adherence issues simultaneously.
Accurate estimation of intervention effects, particularly the complier average causal effect, requires accounting for these intertwined factors.
The proposed ML-EM approach provides a valid framework for analyzing such complex trial designs.