Target Trial Emulation for Regulatory and Clinical Decision Making in Cancer.

However, trials cannot answer every important clinical question.

然而，试验无法回答每一个重要的临床问题。

Evidence gaps often persist in under-represented or small patient populations and for questions about head-to-head comparisons of active treatments, complex treatment strategies (including treatment sequencing and other dynamic treatment strategies), and long-term or rare outcomes.

在代表性不足或患者数量较少的人群中，以及对于活性治疗的直接比较、复杂治疗策略（包括治疗序列和其他动态治疗策略）以及长期或罕见结果等问题上，常常存在证据空白。

To address these questions, clinicians and researchers increasingly turn to observational (real-world) data.

为了解决这些问题，临床医生和研究人员越来越多地转向观察性（真实世界）数据。

The target trial framework provides a systematic approach for designing observational analyses that attempt to emulate a hypothetical pragmatic trial. This process requires that investigators specify the key components of the causal question as elements of the target trial protocol: eligibility criteria, treatment strategies, assignment procedures, follow-up, outcomes, and causal contrasts.

目标试验框架为设计观察性分析提供了一种系统化的方法，这些分析试图模拟一个假设的实用试验。这个过程要求研究者明确指定因果问题的关键组成部分作为目标试验方案的要素：资格标准、治疗策略、分配程序、随访、结果和因果对比。

Explicitly emulating the target trial helps investigators avoid common study design flaws that can lead to immortal time and selection bias.

明确模仿目标试验有助于研究者避免常见的研究设计缺陷，这些缺陷可能导致不朽时间和选择偏倚。

Although the target trial framework helps to avoid such design-related biases, it cannot eliminate biases due to inherent data limitations, such as confounding and measurement error.

尽管目标试验框架有助于避免此类设计相关偏差，但它无法消除由于固有数据限制导致的偏差，例如混杂因素和测量误差。

Here we review target trial emulation to supplement evidence from randomized trials and inform regulatory and clinical decision making in oncology.

在这里，我们回顾了目标试验模拟，以补充随机试验的证据，并为肿瘤学的监管和临床决策提供信息。

Understanding the strengths and limitations of the target trial framework improves the integration of real-world evidence into modern cancer care.

理解目标试验框架的优势和局限性，有助于将真实世界证据更好地融入现代癌症治疗中。