Effect of imbalance and intracluster correlation coefficient in cluster randomization trials with binary outcomes when the available number of clusters is fixed in advance
In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provid...
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Published in | Contemporary clinical trials Vol. 30; no. 4; pp. 317 - 320 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
01.07.2009
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Abstract | In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (
κ) and the intracluster correlation coefficient (
ρ) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (
κ
=
1.0),
i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (
k
⁎) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (
k
⁎) is at least 20 and the imbalance parameter (
κ) is at least 0.8. Empirical powers were close to the nominal level when (
ρ
≤
0.02,
κ
≥
0.8, and
k
⁎
=
10) or (
ρ
≤
0.02,
κ
=
0.8, and
k
⁎
=
20). |
---|---|
AbstractList | In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (
κ) and the intracluster correlation coefficient (
ρ) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (
κ
=
1.0),
i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (
k
⁎) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (
k
⁎) is at least 20 and the imbalance parameter (
κ) is at least 0.8. Empirical powers were close to the nominal level when (
ρ
≤
0.02,
κ
≥
0.8, and
k
⁎
=
10) or (
ρ
≤
0.02,
κ
=
0.8, and
k
⁎
=
20). In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (kappa) and the intracluster correlation coefficient (rho) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (kappa=1.0), i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (k*) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (k*) is at least 20 and the imbalance parameter (kappa) is at least 0.8. Empirical powers were close to the nominal level when (rho< or =0.02, kappa> or =0.8, and k*=10) or (rho< or =0.02, kappa=0.8, and k*=20).In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (kappa) and the intracluster correlation coefficient (rho) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (kappa=1.0), i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (k*) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (k*) is at least 20 and the imbalance parameter (kappa) is at least 0.8. Empirical powers were close to the nominal level when (rho< or =0.02, kappa> or =0.8, and k*=10) or (rho< or =0.02, kappa=0.8, and k*=20). In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [ 1 ] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability ( κ ) and the intracluster correlation coefficient ( ρ ) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case ( κ = 1.0), i.e. , equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group ( k *) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group ( k *) is at least 20 and the imbalance parameter ( κ ) is at least 0.8. Empirical powers were close to the nominal level when ( ρ ≤ 0.02, κ ≥ 0.8, and k * = 10) or ( ρ ≤ 0.02, κ = 0.6, and k * = 20). In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (kappa) and the intracluster correlation coefficient (rho) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (kappa=1.0), i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (k*) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (k*) is at least 20 and the imbalance parameter (kappa) is at least 0.8. Empirical powers were close to the nominal level when (rho< or =0.02, kappa> or =0.8, and k*=10) or (rho< or =0.02, kappa=0.8, and k*=20). Abstract In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability ( κ ) and the intracluster correlation coefficient ( ρ ) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case ( κ = 1.0), i.e. , equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group ( k⁎ ) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group ( k⁎ ) is at least 20 and the imbalance parameter ( κ ) is at least 0.8. Empirical powers were close to the nominal level when ( ρ ≤ 0.02, κ ≥ 0.8, and k⁎ = 10) or ( ρ ≤ 0.02, κ = 0.8, and k⁎ = 20). |
Author | Hu, Fan Ahn, Daniel Skinner, Celette Sugg Ahn, Chul |
AuthorAffiliation | a Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX b Department of Statistical Science, Southern Methodist University, Dallas, TX c Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX |
AuthorAffiliation_xml | – name: b Department of Statistical Science, Southern Methodist University, Dallas, TX – name: c Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX – name: a Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX |
Author_xml | – sequence: 1 givenname: Chul surname: Ahn fullname: Ahn, Chul email: chul.ahn@utsouthwestern.edu organization: Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, United States – sequence: 2 givenname: Fan surname: Hu fullname: Hu, Fan organization: Department of Statistical Science, Southern Methodist University, Dallas, TX, United States – sequence: 3 givenname: Celette Sugg surname: Skinner fullname: Skinner, Celette Sugg organization: Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, United States – sequence: 4 givenname: Daniel surname: Ahn fullname: Ahn, Daniel organization: Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, United States |
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Cites_doi | 10.1111/j.1525-1497.2005.40115.x 10.1016/j.jclinepi.2003.12.013 10.1002/bimj.4710290212 10.1093/biomet/85.2.487 10.1002/sim.2733 10.1016/0895-4356(91)90084-M |
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Keywords | Intracluster correlation Varying cluster size sample size Binary outcomes Correlation coefficient Correlation Randomization Statistical analysis Prognosis Sample size Clinical trial Cluster |
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References | Skinner, Rawl, Moser (bib5) 2005; 20 Lunn, Davies (bib9) 1998; 85 Snedecor, Cochran (bib4) 1989 Taljaard, Donner, Klar (bib2) 2007; 26 Donner, Klar (bib1) 2000 Johnson, Kotz (bib8) 1969 Donner, Eliasziw (bib10) 1991; 44 Adams, Gulliford, Ukoumunne, Eldridge, Chinn, Campbell (bib6) 2004; 57 Pocock (bib3) 1983 Donner, Koval (bib7) 1987; 29 Adams (10.1016/j.cct.2009.03.007_bib6) 2004; 57 Johnson (10.1016/j.cct.2009.03.007_bib8) 1969 Taljaard (10.1016/j.cct.2009.03.007_bib2) 2007; 26 Lunn (10.1016/j.cct.2009.03.007_bib9) 1998; 85 Pocock (10.1016/j.cct.2009.03.007_bib3) 1983 Donner (10.1016/j.cct.2009.03.007_bib10) 1991; 44 Skinner (10.1016/j.cct.2009.03.007_bib5) 2005; 20 Donner (10.1016/j.cct.2009.03.007_bib1) 2000 Snedecor (10.1016/j.cct.2009.03.007_bib4) 1989 Donner (10.1016/j.cct.2009.03.007_bib7) 1987; 29 |
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Snippet | In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner... Abstract In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical... |
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SubjectTerms | Binary outcomes Biological and medical sciences Cardiovascular Clinical trial. Drug monitoring Cluster Analysis Computerized, statistical medical data processing and models in biomedicine Data Interpretation, Statistical Epidemiology General aspects General pharmacology Hematology, Oncology and Palliative Medicine Humans Intracluster correlation Medical sciences Medical statistics Methodology Models, Statistical Pharmacology. Drug treatments Public health. Hygiene Public health. Hygiene-occupational medicine Randomized Controlled Trials as Topic - methods sample size Varying cluster size |
Title | Effect of imbalance and intracluster correlation coefficient in cluster randomization trials with binary outcomes when the available number of clusters is fixed in advance |
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