Gerard J P van Breukelen1, Math J J M Candel. 1. Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands. gerard.vbreukelen@maastrichtuniversity.nl
Abstract
OBJECTIVE: Simple guidelines for calculating efficient sample sizes in cluster randomized trials with unknown intraclass correlation (ICC) and varying cluster sizes. METHODS: A simple equation is given for the optimal number of clusters and sample size per cluster. Here, optimal means maximizing power for a given budget or minimizing total cost for a given power. The problems of cluster size variation and specification of the ICC of the outcome are solved in a simple yet efficient way. RESULTS: The optimal number of clusters goes up, and the optimal sample size per cluster goes down as the ICC goes up or as the cluster-to-person cost ratio goes down. The available budget, desired power, and effect size only affect the number of clusters and not the sample size per cluster, which is between 7 and 70 for a wide range of cost ratios and ICCs. Power loss because of cluster size variation is compensated by sampling 10% more clusters. The optimal design for the ICC halfway the range of realistic ICC values is a good choice for the first stage of a two-stage design. The second stage is needed only if the first stage shows the ICC to be higher than assumed. CONCLUSION: Efficient sample sizes for cluster randomized trials are easily computed, provided the cost per cluster and cost per person are specified.
OBJECTIVE: Simple guidelines for calculating efficient sample sizes in cluster randomized trials with unknown intraclass correlation (ICC) and varying cluster sizes. METHODS: A simple equation is given for the optimal number of clusters and sample size per cluster. Here, optimal means maximizing power for a given budget or minimizing total cost for a given power. The problems of cluster size variation and specification of the ICC of the outcome are solved in a simple yet efficient way. RESULTS: The optimal number of clusters goes up, and the optimal sample size per cluster goes down as the ICC goes up or as the cluster-to-person cost ratio goes down. The available budget, desired power, and effect size only affect the number of clusters and not the sample size per cluster, which is between 7 and 70 for a wide range of cost ratios and ICCs. Power loss because of cluster size variation is compensated by sampling 10% more clusters. The optimal design for the ICC halfway the range of realistic ICC values is a good choice for the first stage of a two-stage design. The second stage is needed only if the first stage shows the ICC to be higher than assumed. CONCLUSION: Efficient sample sizes for cluster randomized trials are easily computed, provided the cost per cluster and cost per person are specified.
Authors: Siyun Yang; Fan Li; Monique A Starks; Adrian F Hernandez; Robert J Mentz; Kingshuk R Choudhury Journal: Stat Med Date: 2020-08-21 Impact factor: 2.373
Authors: Margareth Crisóstomo Portela; Peter J Pronovost; Thomas Woodcock; Pam Carter; Mary Dixon-Woods Journal: BMJ Qual Saf Date: 2015-03-25 Impact factor: 7.035
Authors: Katja Ryynänen; Petteri Oura; Anna-Sofia Simula; Riikka Holopainen; Maija Paukkunen; Mikko Lausmaa; Jouko Remes; Neill Booth; Antti Malmivaara; Jaro Karppinen Journal: Scand J Work Environ Health Date: 2021-04-13 Impact factor: 5.024
Authors: Annerika H M Slok; Johannes C C M In 't Veen; Niels H Chavannes; Thys van der Molen; Maureen Pmh Rutten-van Mölken; Huib A M Kerstjens; Guus M Asijee; Philippe L Salomé; Sebastiaan Holverda; Richard P N Dekhuijzen; Denise Schuiten; Gerard van Breukelen; Daniel Kotz; Onno C P van Schayck Journal: BMC Pulm Med Date: 2014-08-07 Impact factor: 3.317