Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic,Spatial Statistics

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Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic
Spatial Statistics ( IF 2.1 ) Pub Date : 2020-03-23 , DOI: 10.1016/j.spasta.2020.100438
Timothy M Pollington _{1,

2} , Michael J Tildesley ₃ , T Déirdre Hollingsworth ₂ , Lloyd A C Chapman ₄

Affiliation

The tau statistic $τ$ uses geolocation and, usually, symptom onset time to assess global spatiotemporal clustering from epidemiological data. We test different factors that could affect graphical hypothesis tests of clustering or bias clustering range estimates based on the statistic, by comparison with a baseline analysis of an open access measles dataset.

From re-analysing this data we find that the spatial bootstrap sampling method used to construct the confidence interval for the tau estimate and confidence interval (CI) type can bias clustering range estimates. We suggest that the bias-corrected and accelerated (BCa) CI is essential for asymmetric sample bootstrap distributions of tau estimates.

We also find evidence against no spatiotemporal clustering, $p -value \in [0, 0 ⋅ 014]$ (global envelope test). We develop a tau-specific modification of the Loh & Stein spatial bootstrap sampling method, which gives more precise bootstrapped tau estimates and a 20% higher estimated clustering endpoint than previously published (36⋅0 m, 95% BCa CI (14⋅9, 46⋅6), vs 30 m) and an equivalent increase in the clustering area of elevated disease odds by 44%. What appears a modest radial bias in the range estimate is more than doubled on the areal scale, which public health resources are proportional to. This difference could have important consequences for control.

Correct practice of hypothesis testing of no clustering and clustering range estimation of the tau statistic are illustrated in the Graphical abstract. We advocate proper implementation of this useful statistic, ultimately to reduce inaccuracies in control policy decisions made during disease clustering analysis.

中文翻译：

使用 tau 统计量评估时空疾病聚类时统计推断的进展

tau 统计量 $τ$ 使用地理位置和通常的症状发作时间来评估流行病学数据的全球时空聚类。通过与开放获取麻疹数据集的基线分析进行比较，我们测试了可能影响基于统计的聚类图形假设检验或偏差聚类范围估计的不同因素。

通过重新分析这些数据，我们发现用于构建 tau 估计和置信区间 (CI) 类型置信区间的空间引导采样方法可能会使聚类范围估计产生偏差。我们认为偏差校正和加速 (BCa) CI 对于 tau 估计值的非对称样本引导分布至关重要。

我们还发现了不存在时空聚类的证据， $p -价值 ε [0, 0 ⋅ 014]$ （全局包络测试）。我们开发了 Loh & Stein 空间引导采样方法的 tau 特定修改，该方法提供了更精确的引导 tau 估计，并且比之前发布的估计聚类端点高 20%（36⋅0 m，95% BCa CI（14⋅9， 46⋅6)，与 30 m) 相比，疾病几率增加的聚集区域相应增加了 44%。范围估计中看似适度的径向偏差在区域规模上增加了一倍多，而公共卫生资源与之成正比。这种差异可能会对控制产生重要影响。