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Prediction-driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda
Statistics in Medicine ( IF 2 ) Pub Date : 2021-05-28 , DOI: 10.1002/sim.9022
Adam Brand 1 , Susanne May 2 , James P Hughes 2 , Gertrude Nakigozi 3 , Steven J Reynolds 4, 5 , Erin E Gabriel 1
Affiliation  

Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources, as is true with HIV monitoring in resource-limited settings. Pooled testing methods have been developed in order to allow regular testing for all while reducing resource burden. However, the most commonly used methods do not make use of covariate information predictive of treatment failure, which could improve performance. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance. We then compare these methods in the HIV treatment management setting to current methods with respect to testing efficiency, sensitivity, and number of testing rounds using simulated data and data collected in Rakai, Uganda. Results show that the prediction-driven methods increase efficiency by up to 20% compared with current methods while maintaining equivalent sensitivity and reducing number of testing rounds by up to 70%. When predictions were incorrect, the performance of prediction-based matrix methods remained robust. The best performing method using our motivating data from Rakai was a prediction-driven hybrid method, maintaining sensitivity over 96% and efficiency over 75% in likely scenarios. If these methods perform similarly in the field, they may contribute to improving mortality and reducing transmission in resource-limited settings. Although we evaluate our proposed pooling methods in the HIV treatment setting, they can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision.

中文翻译:

预测驱动的汇总测试方法:在乌干达拉凯的 HIV 治疗监测中的应用

慢性疾病通常需要定期检查才能进行适当的治疗。由于资源有限,对所有受感染个体进行定期检测可能不可行,在资源有限的环境中进行 HIV 监测也是如此。已经开发了汇总测试方法,以便在减少资源负担的同时对所有人进行定期测试。然而,最常用的方法没有利用协变量信息来预测治疗失败,这可以提高性能。我们提出并评估了四种预测驱动的合并测试方法,这些方法结合了协变量信息以提高合并测试的性能。然后,我们将 HIV 治疗管理环境中的这些方法与当前方法在检测效率、敏感性、以及使用模拟数据和在乌干达拉凯收集的数据的测试轮数。结果表明,与当前方法相比,预测驱动方法将效率提高了 20%,同时保持了相同的灵敏度并将测试轮数减少了 70%。当预测不正确时,基于预测的矩阵方法的性能保持稳健。使用我们来自 Rakai 的激励数据的最佳执行方法是预测驱动的混合方法,在可能的情况下保持超过 96% 的灵敏度和超过 75% 的效率。如果这些方法在现场表现相似,它们可能有助于在资源有限的环境中提高死亡率并减少传播。尽管我们在 HIV 治疗环境中评估了我们提出的汇集方法,
更新日期:2021-07-19
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