The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions
Pages 2-7
Denise Esserman, Heather G. Allore and Thomas G. Travison
DOI: http://dx.doi.org/10.6000/1929-6029.2016.05.01.1
Published: 08 January 2016

Abstract: Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results.

CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level.

In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters.

Table of Contents

Volume 5 No.1,  2016

* This issue includes papers published with

Special Issue

Methods for Estimating Treatment Effects of Persons with Multiple Chronic Conditions

Heather G Allore, PhD (Guest Editor) Associate Professor Director of Biostatistics, Yale Program on Aging Yale School of Medicine, Department of Internal Medicine 300 George St, 7th FL, New Haven, CT 06511 USA

Editorial: Special Issue: Methods for Estimating Treatment Effects for Persons with Multiple Chronic Conditions - Page 1
Heather Allore
Yale Program on Aging, 300 George St, Suite 775, New Haven, CT 06511 USA

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions - Pages 2-7
Denise Esserman, Heather G. Allore and Thomas G. Travison
http://dx.doi.org/10.6000/1929-6029.2016.05.01.1

Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients - Pages 8-16
Ling Han, M.A. Pisani, K.L.B. Araujo and Heather G. Allore
http://dx.doi.org/10.6000/1929-6029.2016.05.01.2

The Validity of Disease-Specific Quality of Life Attributions Among Adults with Multiple Chronic Conditions - Pages 17-40
John E. Ware Jr., Barbara Gandek and Jeroan Allison
http://dx.doi.org/10.6000/1929-6029.2016.05.01.3

An Empirical Method of Detecting Time-Dependent Confounding: An Observational Study of Next Day Delirium in a Medical ICU - Pages 41-47
T.E. Murphy, P.H. Van Ness, K.L.B. Araujo and M.A. Pisani
http://dx.doi.org/10.6000/1929-6029.2016.05.01.4

Individualized Absolute Risk Calculations for Persons with Multiple Chronic Conditions: Embracing Heterogeneity, Causality, and Competing Events - Pages 48-55
Heather Allore, Gail McAvay, Carlos A. Vaz Fragoso and Terrence E. Murphy
http://dx.doi.org/10.6000/1929-6029.2016.05.01.5

General Articles

Can a Mendelian Randomization Study Predict the Results of a Clinical Trial? Yes and No - Pages 56-61
Antonio Abbate, Charles A. Dinarello, Mariangela Peruzzi, Sebastiano Sciarretta, Giacomo Frati and Giuseppe Biondi-Zoccai
http://dx.doi.org/10.6000/1929-6029.2016.05.01.6

Higher Performance of QuantiFERON TB Compared to Tuberculin Skin Test in Latent Tuberculosis Infection Prospective Diagnosis - Pages 62-70
Tarak Dhaouadi, Imen Sfar, Leila Mouelhi, Raoudha Tekaya, Ines Mahmoud, Jalila Bargaoui, Raoudha Daghbouj, Leila Abdelmoula, Taoufik Najjar, Taieb Ben Abdallah and Yousr Gorgi
http://dx.doi.org/10.6000/1929-6029.2016.05.01.7