Introduced in 2010, the sub-discipline of gerontologic biostatistics was conceptualized to address the specific challenges of analyzing data from clinical research studies involving older adults. Since then, the evolving technological landscape has led to a proliferation of advancements in biostatistics and other data sciences that have significantly influenced the practice of gerontologic research, including studies beyond the clinic. Data science is the field at the intersection of statistics and computer science, and although the term “data science” was not widely used in 2010, the field has quickly made palpable impacts on gerontologic research. In this Review in Depth, we describe multiple advancements of biostatistics and data science that have been particularly impactful. Moreover, we propose the sub-discipline of “gerontologic biostatistics and data science”, or GBDS, which subsumes gerontologic biostatistics into a more encompassing practice. Prominent GBDS advancements that we discuss herein include cutting-edge methods in experimental design and causal inference, adaptations of machine learning, the rigorous quantification of deep phenotypic measurement, and analysis of high-dimensional -omics data. We additionally describe the need for integration of information from multiple studies and propose strategies to foster reproducibility, replicability, and open science. Lastly, we provide information on software resources for GBDS practitioners to apply these approaches to their own work and propose areas where further advancement is needed. The methodological topics reviewed here aim to enhance data-rich research on aging and foster the next generation of gerontologic researchers.

中文翻译：

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老年生物统计学与数据科学：大数据时代的老龄化研究


老年学生物统计学的子学科于 2010 年推出，旨在解决分析涉及老年人的临床研究数据的具体挑战。从那时起，不断发展的技术格局导致生物统计学和其他数据科学的进步激增，这些进步对老年学研究的实践产生了重大影响，包括临床以外的研究。数据科学是统计学和计算机科学的交叉领域，尽管“数据科学”一词在 2010 年没有被广泛使用，但该领域很快就对老年学研究产生了明显影响。在这篇深度评论中，我们描述了生物统计学和数据科学的多项特别有影响力的进步。此外，我们提出了“老年学生物统计学和数据科学”或 GBDS 的子学科，它将老年学生物统计学归入一个更全面的实践中。我们在本文中讨论的突出 GBDS 进展包括实验设计和因果推理的尖端方法、机器学习的适应、深度表型测量的严格量化以及高维组学数据的分析。我们还描述了整合来自多项研究的信息的必要性，并提出了促进可重复性、可复制性和开放科学的策略。最后，我们为 GBDS 从业者提供有关软件资源的信息，以便将这些方法应用于他们自己的工作，并提出需要进一步改进的领域。这里回顾的方法论主题旨在加强关于老龄化的数据丰富的研究并培养下一代老年学研究人员。