论文标题
主动信息，缺少数据和流行率估计
Active information, missing data and prevalence estimation
论文作者
论文摘要
本文的主题是从主动信息的角度来看的普遍性估计。在测试个体中的患病率是在假设个体对疾病进行测试的意愿随着症状的强度增加的假设，存在上升偏见。由于测试偏差而引起的主动信息量化了测试意愿与感染状态相关的程度。将不完整的测试解释为缺失的数据问题，丢失机制会影响原始患病率估计值的偏差的程度。当调整测试偏置时，患病率的降低会因偏置校正而转化为主动信息，并且由于测试偏差而与主动信息相反。患病率和主动信息估计是渐近正常的，这种行为也通过模拟说明。
The topic of this paper is prevalence estimation from the perspective of active information. Prevalence among tested individuals has an upward bias under the assumption that individuals' willingness to be tested for the disease increases with the strength of their symptoms. Active information due to testing bias quantifies the degree at which the willingness to be tested correlates with infection status. Interpreting incomplete testing as a missing data problem, the missingness mechanism impacts the degree at which the bias of the original prevalence estimate can be removed. The reduction in prevalence, when testing bias is adjusted for, translates into an active information due to bias correction, with opposite sign to active information due to testing bias. Prevalence and active information estimates are asymptotically normal, a behavior also illustrated through simulations.
