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Introduction The tau statistic is a recent second-order correlation function that can assess the magnitude and range of global spatiotemporal clustering from epidemiological data containing geolocations of individual cases and, usually, disease onset times. This is the first review of its use, and the aspects of its computation and presentation that could affect inferences drawn and bias estimates of the statistic. Methods Using Google Scholar we searched papers or preprints that cited the papers that first defined/reformed the statistic. We tabulated their key characteristics to understand the statistic's development since 2012. Results Only half of the 16 studies found were considered to be using true tau statistics, but their inclusion in the review still provided important insights into their analysis motivations. All papers that used graphical hypothesis testing and parameter estimation used incorrect methods. There is a lack of clarity over how to choose the time-relatedness interval to relate cases and the distance band set, that are both required to calculate the statistic. Some studies demonstrated nuanced applications of the tau statistic in settings with unusual data or time relation variables, which enriched understanding of its possibilities. A gap was noticed in the estimators available to account for variable person-time at risk. Discussion Our review comprehensively covers current uses of the tau statistic for descriptive analysis, graphical hypothesis testing, and parameter estimation of spatiotemporal clustering. We also define a new estimator of the tau statistic for disease rates. For the tau statistic there are still open questions on its implementation which we hope this review inspires others to research. |
