Ackerman Benjamin, Gan Ryan W, Zhang Youyi, Siddique Juned, Roose James, Lund Jennifer L, Weberpals Janick, Wang Jocelyn R, Meyer Craig S, Hayden Jennifer, Sarsour Khaled, Batavia Ashita S
Johnson & Johnson, Raritan, NJ, USA.
Preventive Medicine and Psychiatry and Behavioral Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Epidemiol Methods. 2025 Sep 26;14(1):20250009. doi: 10.1515/em-2025-0009. eCollection 2025 Jan.
In drug development, there is increasing interest in leveraging real-world data (RWD) to augment trial data and generate evidence about treatment efficacy. However, comparing patient outcomes across trial and routine clinical care settings can be susceptible to bias, namely due to differences in how and when disease assessments occur. This can introduce measurement error in RWD relative to trial standards and lead to bias when comparing endpoints. We develop a novel statistical method, survival regression calibration (SRC), to mitigate measurement error bias in time-to-event RWD outcomes and improve inferences when combining trials with RWD in oncology.
SRC extends upon existing regression calibration methods to address measurement error in time-to-event RWD outcomes. The method entails fitting separate Weibull regression models using trial-like ('true') and real-world-like ('mismeasured') outcome measures in a validation sample, and then calibrating parameter estimates in the full study according to the estimated bias in Weibull parameters. We evaluate performance of SRC under varying degrees of existing measurement error bias via simulation, and then illustrate how SRC can address measurement error when estimating median progression-free survival (mPFS) in newly diagnosed multiple myeloma RWD.
When measurement error exists between trial and real-world mPFS, SRC can effectively account for its resulting bias. SRC yields greater reduction in measurement error bias than standard regression calibration methods, due to its suitability for time-to-event outcomes.
Outcome measurement error is important to address when combining trials and RWD, as it may lead to biased results. Our SRC method helps mitigate such bias, improving comparability between real-world and trial endpoints and strengthening evidence of treatment efficacy.
在药物研发中，利用真实世界数据（RWD）来补充试验数据并生成关于治疗效果的证据的兴趣与日俱增。然而，比较试验和常规临床护理环境中的患者结局可能容易产生偏差，这主要是由于疾病评估的方式和时间存在差异。这可能会在RWD中相对于试验标准引入测量误差，并在比较终点时导致偏差。我们开发了一种新颖的统计方法，即生存回归校准（SRC），以减轻事件发生时间RWD结局中的测量误差偏差，并在肿瘤学中将试验与RWD结合时改善推断。
SRC在现有回归校准方法的基础上进行扩展，以解决事件发生时间RWD结局中的测量误差。该方法需要在验证样本中使用类似试验的（“真实”）和类似真实世界的（“测量错误”）结局测量值拟合单独的威布尔回归模型，然后根据威布尔参数中的估计偏差在整个研究中校准参数估计值。我们通过模拟评估了SRC在不同程度的现有测量误差偏差下的性能，然后说明了SRC在估计新诊断的多发性骨髓瘤RWD中的中位无进展生存期（mPFS）时如何解决测量误差。
当试验和真实世界的mPFS之间存在测量误差时，SRC可以有效地考虑其产生的偏差。由于SRC适用于事件发生时间结局，因此与标准回归校准方法相比，它在测量误差偏差方面的降低幅度更大。
在结合试验和RWD时，结局测量误差很重要，因为它可能导致结果出现偏差。我们的SRC方法有助于减轻这种偏差，提高真实世界和试验终点之间的可比性，并加强治疗效果的证据。
