在HIV近期感染分类中纳入自我报告数据的似然方法
A Likelihood Approach to Incorporating Self-Report Data in HIV Recency Classification
AI总结：
针对HIV近期与长期感染难以区分的问题，提出基于似然的概率模型，结合自我报告检测史与生物标志物进行分类，在多国数据上比现有方法更有效、偏差更小且更稳健。
摘要：
由于难以区分近期感染和长期感染，估算新的HIV感染具有重要意义但充满挑战。我们证明，HIV近期感染状态（近期与长期）可以通过自我报告检测史和生物标志物的组合来确定，这些数据在生物行为调查中越来越容易获得。鉴于自我报告检测史，HIV近期感染状态是部分可观测的。例如，一年前检测出HIV阳性的人应该属于长期感染。基于Population-based HIV Impact Assessment (PHIA) Project收集的全国代表性样本，我们提出了一种基于似然的HIV近期感染分类概率模型。该模型结合了标记和未标记数据，并通过一组逻辑回归模型，整合了HIV近期感染状态如何依赖于生物标志物的机制，以及HIV近期感染状态与最近一次HIV检测的自我报告时间如何共同影响检测结果的机制。我们在Malawi、Zimbabwe和Zambia的PHIA数据以及模拟数据上，将我们的方法与逻辑回归和二分类树（当前做法）进行了比较。我们的模型获得了更有效且偏差更小的参数估计，并且对潜在的报告误差和模型误设具有相对稳健性。
英文摘要：
Estimating new HIV infections is significant yet challenging due to the difficulty in distinguishing between recent and long-term infections. We demonstrate that HIV recency status (recent v.s. long-term) could be determined from the combination of self-report testing history and biomarkers, which are increasingly available in bio-behavioral surveys. HIV recency status is partially observed, given the self-report testing history. For example, people who tested positive for HIV over one year ago should have a long-term infection. Based on the nationally representative samples collected by the Population-based HIV Impact Assessment (PHIA) Project, we propose a likelihood-based probabilistic model for HIV recency classification. The model incorporates both labeled and unlabeled data and integrates the mechanism of how HIV recency status depends on biomarkers and the mechanism of how HIV recency status, together with the self-report time of the most recent HIV test, impacts the test results, via a set of logistic regression models. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi, Zimbabwe, and Zambia PHIA data, as well as on simulated data. Our model obtains more efficient and less biased parameter estimates and is relatively robust to potential reporting error and model misspecification.
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