Original reportEstimation of design effects in cluster surveys☆
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Cited by (41)
Intra-cluster correlations in socio-demographic variables and their implications: An analysis based on large-scale surveys in India
2023, SSM - Population HealthCitation Excerpt :In case of high values of ICC, the assumption of a deff of 2 would be untenable for an EPSEM design. To adjust for the loss of precision in such a case, reducing the cluster size is the most effective method (Agarwal, Awasthi, & Walter, 2005; Gulliford et al., 1999; Katz, 1995; Katz & Zeger, 1994). It should also be noted that for a higher value of ICC, the loss of precision of the estimate will be more.
A study protocol for a cluster randomized pragmatic trial for comparing strategies for implementing primary HPV testing for routine cervical cancer screening in a large health care system
2023, Contemporary Clinical TrialsCitation Excerpt :Both assumptions are estimated using internal data of prior cervical cancer screening practice change at KPSC. The design effect was estimated by simulating cluster-randomizations in these prior data as the ratio of squared standard errors of the between-arm differences with vs. without cluster-level random effects. [51] All power calculations were conducted in PASS 14. [52]
From Agadez to Zinder: Estimating coverage of the MenAfriVac™ conjugate vaccine against meningococcal serogroup A in Niger, September 2010 - January 2012
2013, VaccineCitation Excerpt :We estimated general and regional coverage by aggregating the cluster-samples from the districts, using the size of the household and the target district population as survey weights [21]. We calculated DEFF to estimate the loss of precision due to cluster-sampling [22]. We fitted multivariate Poisson regression models with robust variance to assess the association between vaccination status and variables of interest.
Evaluating Imaging and Computer-aided Detection and Diagnosis Devices at the FDA
2012, Academic RadiologyCitation Excerpt :The results of a study by Cole et al (59) are consistent with these biases. More recently, some progress has been made to understand enrichment bias (60), recognizing the relationship to survey sampling and verification bias (61–65). What has been learned is that the direction and magnitude of enrichment bias depends on the correlation between the measurement used to drive the enrichment (eg, screen-film mammographic interpretations at enrollment) and the ROC ratings collected from the imaging devices being compared (eg, screen-film and full-field digital mammographic interpretations in the reader study) (60).
Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data
2005, Journal of Clinical EpidemiologyCitation Excerpt :Smaller clusters generally show greater degrees of clustering ([2], p. 55). Mickey and Goodwin [9], Katz et al. [10], Katz and Zeger [11], and Slymen and Hovell [12] additionally drew attention to the relationship between the prevalence of an outcome and the design effect. In general, more common outcomes are associated with higher design effects [9]; however, there are few empirical data available to quantify this relationship and offer information to researchers who are planning cluster randomized trials.
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This work was prepared under Cooperative Agreement DAN-0045 between the Office of Nutrition of the United States Agency for International Development (USAID) and The International Center for Epidemiologic and Preventive Ophthalmology (ICEPO), and was supported by National Institutes of Health grants S 10-RRO4060 and A125529 and Merck Biostatistics Department program grant. The surveys from which the data in this report were obtained were collaborative projects between ICEPO and the national partners in each country, funded by the Office of Nutrition, USAID (except where noted).
Malawi: The Ministry of Health, Helen Keller International (HKI), and the International Eye Foundation.
Zambia: The National Food and Nutrition Commission, Tropical Disease Research Centre, the Flying Doctor Service, and the Ministry of Health (funded by the International Development Research Center/Canada).
Indonesia: The Directorate of Nutrition, Department of Health, and Helen Keller International.
Nepal: The National Society for the Prevention of Blindness (Nepal Netra Jyoti Sangh).
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The authors wish to acknowledge the computing assistance of Vincent Carey.