The increasing use of internet, social media, wearable and mobile devices, and various e-health services, claims and billing activities, hospital and pharmacy records, product and disease registries, etc, has led to the rapid generation of various types of digital data related to population health. However, the voluminosity and complexity of real-world data urge for the development of more appropriate, sophisticated, robust, and innovative data analytic techniques to make the best use of them. In the medical field, real-world data (RWD) can be defined as data relating to health outcomes or the delivery of healthcare routinely collected in real-world settings.

RWD have several characteristics as compared to data collected from randomized trials in controlled settings. First, RWD are observational, as opposed to data gathered in a controlled setting. Second, many types of RWD are unstructured (e.g., texts, imaging, networks) and at times inconsistent due to entry variations across providers and health systems. Thus, RWD can be messy, incomplete, heterogeneous, and subject to different types of measurement errors and biases. In some cases, the collected data are also an unrepresentative sample of the underlying population, sometimes occurring without notice or lacking information to validate. Third, RWD may be generated in a high-frequency manner (e.g., millisecond levels in ECGs and from wearables), resulting in voluminous and dynamic data. The messiness of RWD is well-recognized; how to improve the data quality and properly use RWD to generate unbiased RWE is a work in progress.

RWD的数量增加以及人工智能（AI）和机器学习（ML）数据分析技术的快速发展以及成本上升以及传统临床试验的公认局限性引起了RWD的极大兴趣研究效率并弥合临床研究与日常实践之间的差距。在COVID-19大流行期间，RWD始终用于生成RWE，以建立Covid-19疫苗接种的有效性，以模拟局部Covid-19控制策略，以研究与公共生活锁定的行为和心理健康变化，以及其他其他人的心理健康变化。。

可以使用广泛的方法来对源自实用试验得出的RWD进行适当有效的使用，这些试验原则上是为了测试在现实世界中临床环境中干预措施的有效性。务实的试验测量了各种类型的结果，主要是以患者为中心的结果，而不是经典的解释试验中典型的可测量症状或标记。由于RWD的特征，已经开发出了解释性试验的新准则和方法，以生成无偏见的RWE来进行决策和因果推断，尤其是对于每项协议分析，可以说与决策目的更相关。

Target trial emulation is the application of trial design and analysis principles from randomized trials to the analysis of observational data. Target trial emulation can be an important tool especially when comparative evaluation is not yet available or feasible in randomized trials. Controlling for selection bias and confounding is key to the validity of this approach because of the lack of randomization and potentially unrecognized baseline differences, and the control group needs to be comparable with the treated group.

就数据分析方法而言，ML技术变得越来越流行，并且是预测建模的强大工具。此外，正在迅速开发新的，更强大的ML技术。还有许多开源代码（例如在GitHub上）和软件库（例如Tensorflow，Pytorch，keras），以促进这些技术的实现。统计建模和推论方法对于理解RWD，获得因果关系，测试/验证假设以及生成监管级RWE以告知决策者和监管机构的监管级，这是必要的。实用试验和目标试验模拟中的动机和设计和分析原理是获得因果推断，除了传统的统计方法之外，更具创新的方法来调整潜在的混杂因素并提高RWD的因果推理的能力。在该方向上的一个众所周知的框架是针对性学习，该学习已成功地用于用于使用EHR数据和COVID-19治疗的疗效的因果推断，以对动态治疗规则进行了针对性的推断。

总之，与对照试验相比，RWD有可能生成有效和公正的RWE，并节省成本和时间的节省，并提高与医疗和健康相关的研究和决策的效率。同时，RWD有局限性。提高数据质量并克服RWD限制以使其充分利用的程序已经并且将继续开发。