@article {Natarajan:2012:0035-9254:653,
title = "An extension of the Wilcoxon rank sum test for complex sample survey data",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
parent_itemid = "infobike://bpl/rssc",
publishercode ="bp",
year = "2012",
volume = "61",
number = "4",
publication date ="2012-08-01T00:00:00",
pages = "653-664",
itemtype = "ARTICLE",
issn = "0035-9254",
eissn = "1467-9876",
url = "https://www.ingentaconnect.com/content/bpl/rssc/2012/00000061/00000004/art00008",
doi = "doi:10.1111/j.1467-9876.2011.01028.x",
author = "Natarajan, Sundar and Lipsitz, Stuart R. and Fitzmaurice, Garrett M. and Sinha, Debajyoti and Ibrahim, Joseph G. and Haas, Jennifer and Gellad, Walid",
abstract = "
Summary. In complex survey sampling, a fraction of a finite population is sampled. Often, the survey is conducted so that each subject in the population has a different probability of being selected into the sample. Further, many complex surveys involve stratification and
clustering. For generalizability of the sample to the finite population, these features of the design are usually incorporated in the analysis. Although the Wilcoxon rank sum test is commonly used to compare an ordinal variable in bivariate analyses, no simple extension of the Wilcoxon rank
sum test has been proposed for complex survey data. With multinomial sampling of independent subjects, the Wilcoxon rank sum test statistic equals the score test statistic for the group effect from a proportional odds cumulative logistic regression model for an ordinal outcome. Using this
regression framework, for complex survey data, we formulate a similar proportional odds cumulative logistic regression model for the ordinal variable, and we use an estimating equations score statistic for no group effect as an extension of the Wilcoxon test. The method proposed is applied
to a complex survey designed to produce national estimates of healthcare use, expenditures, sources of payment and insurance coverage.",
}