Abstract
Tango proposed an index for detecting disease clustering in time applicable to grouped data obtained from a population that remains fairly stable over the study period. In this paper, we show that Tango's index is a two-dimensional U-statistic having an asymptotic normal distribution. To apply this result in the finite sampling situation, an Edgeworth expansion is used and is shown to be at least as accurate as Tango's best result in approximating the tails of his test statistic under the null hypothesis. This is extended to show that the Edgeworth expansion can be used to approximate the power of Tango's test statistic under selected alternatives to randomness. A power study based on simulations is conducted to compare the power of Tango's index to that of three of its competitors.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
