1999
DOI: 10.1093/ije/28.2.319
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Simple sample size calculation for cluster-randomized trials

Abstract: The methods are illustrated using two case studies: an unmatched trial of the impact of impregnated bednets on child mortality in Kenya, and a pair-matched trial of improved sexually-transmitted disease (STD) treatment services for HIV prevention in Tanzania.

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Cited by 736 publications
(646 citation statements)
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“…The study sample size was determined using an individual randomized trial sample calculation formula [21], with 80% statistical power, a 5% significance level, and 1.5 design effect. The proportion of women who delivered in a health facility was used to calculate the sample size.…”
Section: Methodsmentioning
confidence: 99%
“…The study sample size was determined using an individual randomized trial sample calculation formula [21], with 80% statistical power, a 5% significance level, and 1.5 design effect. The proportion of women who delivered in a health facility was used to calculate the sample size.…”
Section: Methodsmentioning
confidence: 99%
“…Considering slide-positive parasite prevalence as a proportion and haemoglobin as a concentration, 35 clusters with 110 children each would have 80% power to detect a 30% reduction in parasitaemia and a 5 g/L increase in haemoglobin at the 5% level of significance if half the child cohort was sampled. Demonstrating a 60% reduction in house entering mosquitoes (A gambiae) associated with IRS-LLIN, with 90% power and at the 5% level of significance, required six houses in each cluster and 16 clusters in each arm of the trial over two years 35 .…”
Section: Sample Size Rationalementioning
confidence: 99%
“…This leads to a design effect of 1.38, and we thus have to randomize at least 13 clusters in each arm. 17 The sample size requirement has been satisfied up to now since each arm involves 13 clusters of an average of 2,744 women…”
Section: Sample Sizementioning
confidence: 99%