Department of Information & Statistics, Yonsei University, Wonju, Korea.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Stat Med. 2021 Jan 30;40(2):287-298. doi: 10.1002/sim.8774. Epub 2020 Oct 21.
In an infectious disease cohort study, individuals who have been infected with a pathogen are often recruited for follow up. The period between infection and the onset of symptomatic disease, referred to as the incubation period, is of interest because of its importance on disease surveillance and control. However, the incubation period is often difficult to ascertain due to the uncertainty associated with asymptomatic infection onset time. An additional complication is that the observed infected subjects are likely to have longer incubation periods due to the prevalent sampling. In this article, we demonstrate how to estimate the distribution of the incubation period with the uncertain infection onset, subject to left-truncation and right-censoring. We employ a family of sufficiently general parametric models, the generalized odds-rate class of regression models, for the underlying incubation period and its correlation with covariates. In simulation studies, we assess the finite sample performance of the model fitting and hazard function estimation. The proposed method is illustrated on data from the HIV/AIDS study on injection drug users admitted to a detoxification program in Badalona, Spain.
在传染病队列研究中,通常会招募已感染病原体的个体进行随访。感染和出现症状性疾病之间的时间段,称为潜伏期,由于其对疾病监测和控制的重要性而受到关注。然而,由于无症状感染开始时间的不确定性,潜伏期通常难以确定。另一个复杂情况是,由于普遍存在的抽样,观察到的感染个体可能具有更长的潜伏期。在本文中,我们展示了如何在存在左截断和右删失的情况下,针对不确定的感染起始时间,估计潜伏期的分布。我们采用了广义几率率回归模型这一充分通用的参数模型族,用于基础潜伏期及其与协变量的相关性。在模拟研究中,我们评估了模型拟合和危险函数估计的有限样本性能。所提出的方法在西班牙巴达洛纳戒毒计划中接受治疗的注射吸毒者的艾滋病毒/艾滋病研究数据上进行了说明。