The case time series design: a new tool for big data analysis

最近大数据技术的发展改变了生物医学研究。例如，在相互关联的电子数据库中收集健康记录，为大量患者样本提供有关人口统计、健康事件、药物和生活方式因素的信息。同样，移动电话等便携式设备提供了招募大量参与者的可能性，并收集实时和地理定位的个人水平测量。虽然这些资源提供了回答传统研究无法解决的研究问题的可能性，但它们需要创新的分析方法。该项目通过开发一种称为案例时间序列设计的新颖分析方法来解决这个问题。这种设计结合了现有方法的特点，产生了一种适应性更强的工具，特别适合分析高信息量的大数据资源。病例时间序列设计适用于不同的研究领域，用于调查与各种风险因素（如环境暴露、临床条件或药物使用）相关的健康影响。研究计划以详细的工作计划构成，其中包括案例时间序列设计的方法发展和说明其在生物医学研究的各个研究领域中的应用的示例。

Biomedical research has been transformed by the recent development of big data technologies. For instance, the collection of health records in linked electronic databases provide information on demographics, health events, medications, and lifestyle factor for large samples of patients. Similarly, portable devices such as mobile phones provide the possibility to recruit large number of participants, and to collect real-time and geo-located individual-level measurements. While these resources offer the possibility to answer research questions that could not be feasibly addressed using traditional studies, they require innovative analytical approaches.This project contributes to address this issue through the development of a novel analytical method called case time series design. This design combines features of existing approaches to generate a more adaptable tool, particularly well-suited for the analysis of highly-informative big data resources. The case time series design is applicable in different research areas for investigating health effects associated with various risk factors, such as environmental exposures, clinical conditions, or drug use. The research proposal is structured in a detailed work plan that includes the methodological development of the case time series design and examples that illustrate its application in various research areas within biomedical research.