STATISTICAL METHODS FOR MISMEASURED OR MISSING DATA

错误测量或丢失数据可能会导致错误或 误导性的结论，并在 各种生物医学领域，包括环境流行病学。 另一方面，通过精心设计研究来利用 有计划的脱靶，研究人员可能会节省资金，同时实现 有效且强大的统计推断。这一项目旨在 开发新的统计方法，并将现有方法应用于 应对有效的统计估计和测试带来的挑战 由于计划外和计划外的数据测量错误或丢失。 故意省略某些数据的策略方面的工作仍在继续 在节省研究成本的同时保留大部分信息 如果所有的数据都被收集了的话就可以使用了。什么时候 测量暴露是昂贵的(例如，测试血清的毒性 代谢物)，化验成本可能会使某些暴露于 学习。我们发现，在病例对照研究中，将样本混合在一起 以一种精心设计的方式从几个人那里降低了检测成本 在统计能力损失可以忽略不计的情况下。

Mismeasured or missing data can lead to false or misleading conclusions and present a widespread problem in a variety of biomedical fields, including environmental epidemiology. On the other hand, by carefully designing studies to make use of planned missingness, researchers may save money while achieving valid and powerful statistical inference. This project seeks to develop new statistical methods and to apply existing methods to address challenges to valid statistical estimation and testing posed by mismeasured or missing data, both unplanned and planned. Work continues on strategies for intentionally omitting some data to save on study costs while preserving most of the information that would have been available had all data been collected. When measuring an exposure is expensive (e.g., testing serum for toxic metabolites), assay cost may make certain exposures impractical to study. We showed that, in case-control studies, pooling samples from several individuals in a carefully designed way cuts assay costs substantially with negligible loss of statistical power.