For large cohort studies with rare outcomes, the nested case-control design only requires data collection of small subsets of the individuals at risk. These are typically randomly sampled at the observed event times and a weighted, stratified analysis takes over the role of the full cohort analysis. Motivated by observational studies on the impact of hospital-acquired infection on hospital stay outcome, we are interested in situations, where not necessarily the outcome is rare, but time-dependent exposure such as the occurrence of an adverse event or disease progression is. Using the counting process formulation of general nested case-control designs, we propose three sampling schemes where not all commonly observed outcomes need to be included in the analysis. Rather, inclusion probabilities may be time-dependent and may even depend on the past sampling and exposure history. A bootstrap analysis of a full cohort data set from hospital epidemiology allows us to investigate the practical utility of the proposed sampling schemes in comparison to a full cohort analysis and a too simple application of the nested case-control design, if the outcome is not rare.

中文翻译：

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嵌套暴露病例对照抽样：一种分析稀有时间相关暴露的抽样方案。

对于结果很少的大型队列研究，嵌套病例对照设计仅需要收集风险个体的一小部分的数据。通常在观察到的事件时间对它们进行随机采样，然后进行加权的分层分析来代替整个队列分析。基于对医院获得性感染对住院时间影响的观察性研究的推动，我们对以下情况感兴趣，在这种情况下，结果不一定是罕见的，而是时间依赖性暴露，例如不良事件的发生或疾病的进展。使用通用嵌套案例控制设计的计数过程公式，我们提出了三种采样方案，其中并非所有通常观察到的结果都需要包括在分析中。相当，包含概率可能与时间有关，甚至可能取决于过去的采样和暴露历史。对医院流行病学的全部队列数据进行引导分析，使我们能够比较提议的抽样方案的实际效用，而不是对整个队列分析和嵌套病例对照设计的过于简单的应用进行比较（如果结果并不罕见） 。