发表机构
Biostatistics Division, College of Public Health, The Ohio State University（俄亥俄州立大学公共卫生学院生物统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传染病传播中时间至事件数据的依赖性，提出惩罚成对加速失效时间模型，用自适应LASSO实现变量选择，模拟和实际数据验证其精度与可解释性。
AI 中文摘要
在传染病传播中，时间至事件结局的回归和变量选择因传播引起的依赖性而变得复杂，这种依赖性违反了标准的独立性假设。成对生存分析通过建模有序对中的接触间隔来解决这种依赖性，接触间隔定义为该对中第一个个体从传染性发作到与第二个个体首次传染性接触的时间。我们提出了一种惩罚成对加速失效时间模型，用于接触间隔，该模型同时容纳传染性和易感性的协变量效应，并联合表示人群内传播和来自外部来源的感染。对于具有许多候选协变量的变量选择，我们开发了自适应LASSO惩罚最大似然估计，并采用坐标下降算法，将分布基线参数视为非惩罚的冗余参数。我们在局部渐近机制下建立了自适应LASSO估计量的选择一致性和渐近正态性，该机制中信息在随访期间所有曾处于传播风险的有序对中累积，并基于轮廓似然正则条件。模拟研究表明，与未惩罚估计量和LASSO相比，该方法具有准确的变量选择和更高的估计精度。我们使用洛杉矶县2009年家庭甲型H1N1流感监测数据来说明该方法，其中自适应LASSO产生了稀疏、可解释的模型，并提高了所选协变量效应估计的精度，包括抗病毒预防对易感性的影响。
英文摘要
In infectious disease transmission, regression and variable selection for time-to-event outcomes are complicated by transmission-induced dependence that violates standard independence assumptions. Pairwise survival analysis addresses this dependence by modeling contact intervals in ordered pairs, defined as the time from the onset of infectiousness in the pair's first individual to their first infectious contact with the second individual. We propose a penalized pairwise accelerated failure time model for contact intervals that accommodates simultaneous covariate effects on infectiousness and susceptibility while jointly representing within-population transmission and infection from external sources. For variable selection with many candidate covariates, we develop adaptive LASSO penalized maximum likelihood estimation with a coordinate descent algorithm, treating distributional baseline parameters as unpenalized nuisance parameters. We establish selection consistency and asymptotic normality of the adaptive LASSO estimator under profile-likelihood regularity conditions in a local asymptotic regime in which information accumulates over all ordered pairs that are ever at risk of transmission during follow-up. Simulation studies show accurate variable selection and improved estimation accuracy compared with the unpenalized estimator and the LASSO. We illustrate the method using 2009 household influenza A (H1N1) surveillance data from Los Angeles County, where the adaptive LASSO produces a sparse, interpretable model and improves the precision of selected covariate effect estimates, including the effect of antiviral prophylaxis on susceptibility.
