Belitskaya-Levy Ilana, Shao Yongzhao, Goldberg Judith D
New York University School ofMedicine, New York, USA.
Int J Biostat. 2008 Jul 30;4(1):Article 15. doi: 10.2202/1557-4679.1046.
Translational research studies often involve a central study (e.g. clinical trial, cohort of patients, etc.) and multiple investigators who are each interested in addressing different research questions using the same patient population. However, it is often impossible for the investigators to include all patients in all of the ancillary translational research substudies that are part of the main study. This arises due to time and budgetary constraints and other logistical considerations. In this paper, we propose a prospective Systematic Missing-At-Random study design (SMAR) with planned partially missing covariates collected using a nested random sampling scheme that allows an integrated statistical analysis across all domains of data. We propose an algorithm for data analysis that incorporates the features of the design. We show that the SMAR design is computationally and statistically efficient as well as cost effective using simulation studies and a published data example. An extension to a two-stage prospective-retrospective design is discussed.
转化研究通常涉及一项核心研究（如临床试验、患者队列等）以及多个研究人员，他们各自都有兴趣利用同一患者群体来解决不同的研究问题。然而，研究人员往往不可能将所有患者纳入作为主要研究一部分的所有辅助转化研究子研究中。这是由于时间和预算限制以及其他后勤方面的考虑因素导致的。在本文中，我们提出了一种前瞻性系统随机缺失研究设计（SMAR），该设计采用嵌套随机抽样方案收集计划部分缺失的协变量，允许对所有数据领域进行综合统计分析。我们提出了一种结合该设计特征的数据分析算法。通过模拟研究和一个已发表的数据示例，我们表明SMAR设计在计算和统计上是高效的，并且具有成本效益。还讨论了对两阶段前瞻性-回顾性设计的扩展。
