Modern clinical trials and cohort studies gather low-cost data on all participants but may have limited resources to assess expensive exposures such as biomarkers or genomic data. When interest lies in associations involving expensive exposures, two-phase designs provide a cost-effective framework by using information available on all participants to guide the targeted selection of a subset for additional measurements. We extend this framework to studies with ordinal outcomes, a common yet previously unexplored setting. We propose three outcome-informed phase 2 sampling designs -- outcome-dependent sampling (ODS), covariate-stratified ODS, and residual-dependent sampling -- that leverage phase 1 data to enrich phase 2 selection with informative subjects. We then develop analysis methods for valid and efficient estimation/inference, including conditional likelihood methods with ascertainment-corrected maximum likelihood estimation, multiple imputation, and a full likelihood method using sieve maximum likelihood estimation. Across a range of scenarios, simulation studies show that the proposed methods substantially improve efficiency over simple random sampling with standard maximum likelihood estimation. We further demonstrate their practical utility by examining the association between interleukin-6 and a four-level clinical status outcome -- discharged, hospitalized but not in the ICU, hospitalized in the ICU, and death -- 14 days after randomization into the Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis trial.

翻译：现代临床试验和队列研究可获取所有参与者的低成本数据，但评估生物标志物或基因组数据等昂贵暴露因素的资源可能有限。当研究聚焦于涉及昂贵暴露因素的关联性时，两阶段设计通过利用所有参与者的可用信息指导目标性亚组选择进行额外测量，提供了一种经济高效的框架。我们将该框架扩展至有序结局研究——这一常见但此前尚未被探索的场景。我们提出三种结局告知的第二阶段抽样设计：结局依赖抽样、协变量分层结局依赖抽样和残差依赖抽样，这些方法利用第一阶段数据丰富第二阶段具有信息价值的受试者选择。随后我们开发了用于有效估计与推断的分析方法，包括基于确证校正极大似然估计的条件似然法、多重插补法，以及采用筛极大似然估计的完全似然法。在多种场景下，模拟研究表明相较于采用标准极大似然估计的简单随机抽样，所提出方法可显著提升效率。我们进一步通过分析Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis试验随机化14天后白细胞介素-6与四水平临床状态结局（出院、住院但未入ICU、入住ICU、死亡）的关联性，展示了该方法的实际应用价值。