Two-phase sampling design is a common practice in many medical studies. Generally, the first-phase classification is fallible but relatively cheap, while the accurate second phase state-of-the-art medical diagnosis is complex and rather expensive to perform. When constructed efficiently it offers great potential for higher true case detection as well as for higher precision at a limited cost. In this article, we consider epidemiological studies with two-phase sampling design. However, instead of a single two-phase study, we consider a scenario where a series of two-phase studies are done in a longitudinal fashion on a cohort of interest. Another major design issue is non-curable pattern of certain disease (e.g. Dementia, Alzheimer's etc.). Thus often the identified disease positive subjects are removed from the original population under observation, as they require clinical attention, which is quite different from the yet unidentified group. In this article, we motivated our methodology development from two real-life studies. We consider efficient and simultaneous estimation of prevalence as well incidence at multiple time points from a sampling design-based approach. We have explicitly shown the benefit of our developed methodology for an elderly population with significant burden of home-health care usage and at the high risk of major depressive disorder.

中文翻译：

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关于两阶段纵向抽样设计中发病率和患病率的估计。

两阶段采样设计是许多医学研究中的常见实践。通常，第一阶段的分类是容易犯错的，但相对便宜，而准确的第二阶段的最新医学诊断是复杂的，并且执行起来相当昂贵。当有效地构造时，它为以更高的成本提供更高的真实案例检测和更高的精度提供了巨大潜力。在本文中，我们考虑采用两阶段抽样设计进行流行病学研究。但是，我们不考虑单个的两阶段研究，而是针对一个感兴趣的群体纵向进行一系列两阶段研究的方案。另一个主要的设计问题是某些疾病（例如痴呆，阿尔茨海默氏病等）的不可治愈的模式。因此，通常将确定的疾病阳性受试者从观察中的原始人群中移出，因为他们需要临床关注，这与尚未确定的人群完全不同。在本文中，我们从两项实际研究中激发了方法的发展。我们考虑从基于抽样设计的方法中，在多个时间点对流行率以及发生率进行有效且同时的估计。我们已经明确显示了我们开发的方法的好处，该方法适用于家庭医疗保健负担较重且患有严重抑郁症的高风险人群。我们考虑从基于抽样设计的方法中，在多个时间点对流行率以及发生率进行有效且同时的估计。我们已经明确显示了我们开发的方法的好处，该方法适用于家庭医疗保健负担较重且患有严重抑郁症的高风险人群。我们考虑从基于抽样设计的方法中，在多个时间点对流行率以及发生率进行有效且同时的估计。我们已经明确显示了我们开发的方法的好处，该方法适用于家庭医疗保健负担较重且患有严重抑郁症的高风险人群。