Improved Procedures for Estimation of Disease Prevalence Using Ranked Set Sampling
- 27 August 2007
- journal article
- research article
- Published by Wiley in Biometrical Journal
- Vol. 49 (4), 530-538
- https://doi.org/10.1002/bimj.200610302
Abstract
Ranked set sampling (RSS) is a sampling procedure that can be considerably more efcient than simple random sampling (SRS). When the variable of interest is binary, ranking of the sample observations can be implemented using the estimated probabilities of success obtained from a logistic regression model developed for the binary variable. The main objective of this study is to use substantial data sets to investigate the application of RSS to estimation of a proportion for a population that is different from the one that provides the logistic regression. Our results indicate that precision in estimation of a population proportion is improved through the use of logistic regression to carry out the RSS ranking and, hence, the sample size required to achieve a desired precision is reduced. Further, the choice and the distribution of covariates in the logistic regression model are not overly crucial for the performance of a balanced RSS procedure.Keywords
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