Lin Feng-Chang, Li Quefeng, Lin Jessica T
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.
Institute of Global Health and Infectious Diseases, University of North Carolina, Chapel Hill, North Carolina.
Biometrics. 2020 Dec;76(4):1351-1363. doi: 10.1111/biom.13226. Epub 2020 Mar 3.
In patients with Plasmodium vivax malaria treated with effective blood-stage therapy, the recurrent illness may occur due to relapse from latent liver-stage infection or reinfection from a new mosquito bite. Classification of the recurrent infection as either relapse or reinfection is critical when evaluating the efficacy of an anti-relapse treatment. Although one can use whether a shared genetic variant exists between baseline and recurrence genotypes to classify the outcome, little has been suggested to use both sharing and nonsharing variants to improve the classification accuracy. In this paper, we develop a novel classification criterion that utilizes transition likelihoods to distinguish relapse from reinfection. When tested in extensive simulation experiments with known outcomes, our classifier has superior operating characteristics. A real data set from 78 Cambodian P. vivax malaria patients was analyzed to demonstrate the practical use of our proposed method.
在用有效的血液阶段疗法治疗间日疟原虫疟疾患者时，复发性疾病可能由于潜伏性肝期感染的复发或新蚊虫叮咬导致的再感染而发生。在评估抗复发治疗的疗效时，将复发性感染分类为复发或再感染至关重要。虽然可以使用基线基因型和复发基因型之间是否存在共享遗传变异来对结果进行分类，但很少有人建议使用共享和非共享变异来提高分类准确性。在本文中，我们开发了一种利用转移似然性来区分复发和再感染的新型分类标准。在具有已知结果的广泛模拟实验中进行测试时，我们的分类器具有卓越的操作特性。分析了来自78名柬埔寨间日疟原虫疟疾患者的真实数据集，以证明我们提出的方法的实际应用。
