Collaborative Research: Conditional Frailty Duration Model for the Study of Repeated Events in the Social Sciences

重复事件过程，即受试者可能多次经历同一类型的事件，在一系列重要的社会科学应用中无处不在。本研究的中心目标是开发一种稳健的估计策略来评估重复事件生存模型中的协变量效应。作为该策略的核心部分，条件脆弱性模型结合了用于解释异质性（随机效应）和事件依赖性（分层）的替代模型的关键特征，并在同一模型中对风险集进行了条件定义。本研究的目的是进一步检验、扩展和应用条件脆弱性模型。该研究将推动事件历史模型的应用，并通过开发准确模拟异质性和事件依赖性的生存估计器，加深对社会科学中重复事件过程的理解。更具体地说，该研究着眼于条件脆弱性模型在以下情况下的鲁棒性：a)不同程度的审查；B)小样本量；C)时间依赖性；d)事件依赖的替代形式。条件脆弱性模型将被扩展到处理a)多层分析和b)竞争风险。条件脆弱性模型将应用于两个核心的社会科学问题：如何解释a)内战和b)寄养儿童的时间和发生？重复事件生存模型的发展和应用将在社会科学事件研究中得到广泛应用。此外，该方法将帮助公共政策领域的从业者更好地评估政策和条件的作用。

Repeated events processes, where a subject may experience the same type of event multiple times, are ubiquitous across a great range of important social science applications. The central aim of the research is to develop a robust estimation strategy for assessing covariate effects in repeated events survival models. As the center piece of that strategy, the conditional frailty model combines key features of alternative models designed to account for heterogeneity (a random effect) and for event dependence (stratification), with a conditional definition of the risk set in the same model. The goal of the research is to further test, extend, and apply the conditional frailty model.The research will advance the application of event history models and enable a deeper understanding of repeated events processes in the social sciences by developing survival estimators that accurately model heterogeneity and event dependence. More specifically, the research looks at the robustness of the conditional frailty model under complications introduced by a) varying degrees of censoring; b) small sample sizes; c) time dependence; and d) alternative forms of event dependence. The conditional frailty model will be extended to deal with a) multiple levels of analysis and b) competing risks. The conditional frailty model will be applied to two central social science questions: What explains the timing and occurrence of a) civil wars and b) the churning of children in foster care? Advances in the development and application of repeated events survival models will be widely applicable in the study of events in the social sciences. Additionally, the methodology will help practitioners in the public policy arena better assess the role of policies and conditions.