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Table F1Reported vs. effective sample sizes for clustered studies evaluating reduction in HbA1c

No. of clustersReported N(patients)Effective N(patients)Percent reduction (%)
Benjamin 199912 21064657
Boucher 199713 618310543
Olivarius 200114 4848748572
de Sonnaville 199715 2856337633
Frijling 20029 1231430112821
Groeneveld 200116 1522416526
Hetlevik 200017 3073345238
Hirsch 200218 21094658
Kiefe 200119 84135297428
Kinmonth 199810 4224019718
Kogan 200320 4428324912
Litzelman 199321 435311169
Mazzuca 198622 1312710418
McDermott 200123 2167838044
Meigs 200311 6659837737
O'Connor 199624 22416075
Ovhed 200025 23946683
Reed 200126 918912335
Renders 200127 2738929125
Wagner 200128 3460942530
Walker 200129 23456581
Deeb 198830 663617373
Feder 199531 2421210
Legorreta 199632 22055872
Hartmann 199533 1737624635
Walker 200129 23456581
Legorreta 199632 21855570
Branger 199934 3227522717
Mazzuca 198622 1412010116
*

Effective N equals sample size adjusted for presence of clustering. It was calculated as NEffective = (k*m) / (1 + (m-1)*r), where 'k 'is the number of clusters, ‘m’ is the number of patients per cluster, and ‘r’ is the intracluster coefficient (ICC). When r = 0, then NEffective = k*m (i.e., the reported sample size) When r = 1, then NEffective = k(i.e., the number of clusters) 18

From: Appendix F, Calculation of effective sample sizes for trials with clustering

Cover of Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies (Vol. 2: Diabetes Care)
Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies (Vol. 2: Diabetes Care).
Technical Reviews, No. 9.2.
Shojania KG, Ranji SR, Shaw LK, et al.

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