Abstract We consider estimating the conditional prevalence of a disease from data pooled according to the group testing mechanism. Consistent estimators have been proposed in the literature, but they rely on the data being available for all individuals. In infectious disease studies where group testing is frequently applied, the covariate is often missing for some individuals. There, unless the missing mechanism occurs completely at random, applying the existing techniques to the complete cases without adjusting for missingness does not generally provide consistent estimators, and finding appropriate modifications is challenging. We develop a consistent spline estimator, derive its theoretical properties, and show how to adapt local polynomial and likelihood estimators to the missing data problem. We illustrate the numerical performance of our methods on simulated and real examples. Supplementary materials for this article are available online.

中文翻译：

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从具有缺失协变量的组测试数据估计条件流行率

摘要 我们考虑从根据组测试机制收集的数据中估计疾病的条件流行率。文献中已经提出了一致的估计量，但它们依赖于所有个人都可用的数据。在经常应用群体测试的传染病研究中，某些个体经常缺少协变量。在那里，除非缺失机制完全随机发生，否则将现有技术应用于完整案例而不调整缺失通常不会提供一致的估计量，并且找到适当的修改具有挑战性。我们开发了一个一致的样条估计器，推导了它的理论特性，并展示了如何使局部多项式和似然估计器适应缺失数据问题。我们说明了我们的方法在模拟和真实例子上的数值性能。本文的补充材料可在线获取。