A spatial scan statistic for survival data based on generalized life distribution. Issue 19 (1st October 2016)

• Record Type: Journal Article
• Authors: Bhatt, Vijaya; Tiwari, Neeraj
• Is Part Of: Communications in statistics. Volume 45:Issue 19(2016)
• Page Start: 5730
• Page End: 5744
• Publication Date: 2016-10-01
• Subjects: Cluster detection -- Generalized life distribution model -- Monte Carlo simulation -- Spatial scan statistic -- Survival data -- Tuberculosis.; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2014.948207
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: For many years, detection of clusters has been of great public health interest and widely studied. Several methods have been developed to detect clusters and their performance has been evaluated in various contexts. Spatial scan statistics are widely used for geographical cluster detection and inference. Different types of discrete or continuous data can be analyzed using spatial scan statistics for Bernoulli, Poisson, ordinal, exponential, and normal models. In this paper, we propose a scan statistic for survival data which is based on generalized life distribution model that provides three important life distributions, viz. Weibull, exponential, and Rayleigh. The proposed method is applied to the survival data of tuberculosis patients in Nainital district of Uttarakhand, India, for the year 2004–05. The Monte Carlo simulation studies reveal that the proposed method performs well for different survival distributions.