Ackerman Benjamin, Gan Ryan W, Meyer Craig S, Wang Jocelyn R, Zhang Youyi, Hayden Jennifer, Mahoney Grace, Lund Jennifer L, Weberpals Janick, Schneeweiss Sebastian, Roose James, Siddique Juned, Nadeem Omar, Giri Smith, Stürmer Til, Ailawadhi Sikander, Batavia Ashita S, Sarsour Khaled
Janssen Research and Development, LLC, A Johnson and Johnson Company, Raritan, NJ, United States.
Department of Epidemiology, University of North Carolina, Chapel Hill, United States.
Front Drug Saf Regul. 2024 Jul 19;4:1423493. doi: 10.3389/fdsfr.2024.1423493. eCollection 2024.
While randomized controlled trials remain the reference standard for evaluating treatment efficacy, there is an increased interest in the use of external control arms (ECA), namely in oncology, using real-world data (RWD). Challenges related to measurement of real-world oncology endpoints, like progression-free survival (PFS), are one factor limiting the use and acceptance of ECAs as comparators to trial populations. Differences in how and when disease assessments occur in the real-world may introduce measurement error and limit the comparability of real-world PFS (rwPFS) to trial progression-free survival. While measurement error is a known challenge when conducting an externally-controlled trial with real-world data, there is limited literature describing key contributing factors, particularly in the context of multiple myeloma (MM). We distinguish between biases attributed to how endpoints are derived or ascertained (misclassification bias) and when outcomes are observed or assessed (surveillance bias). We further describe how misclassification of progression events (i.e., false positives, false negatives) and irregular assessment frequencies in multiple myeloma RWD can contribute to these biases, respectively. We conduct a simulation study to illustrate how these biases may behave, both individually and together. We observe in simulation that certain types of measurement error may have more substantial impacts on comparability between mismeasured median PFS (mPFS) and true mPFS than others. For instance, when the observed progression events are misclassified as either false positives or false negatives, mismeasured mPFS may be biased towards earlier (mPFS bias = -6.4 months) or later times (mPFS bias = 13 months), respectively. However, when events are correctly classified but assessment frequencies are irregular, mismeasured mPFS is more similar to the true mPFS (mPFS bias = 0.67 months). When misclassified progression events and irregular assessment times occur simultaneously, they may generate bias that is greater than the sum of their parts. Improved understanding of endpoint measurement error and how resulting biases manifest in RWD is important to the robust construction of ECAs in oncology and beyond. Simulations that quantify the impact of measurement error can help when planning for ECA studies and can contextualize results in the presence of endpoint measurement differences.
虽然随机对照试验仍然是评估治疗效果的参考标准，但人们对使用外部对照臂（ECA）的兴趣与日俱增，即在肿瘤学领域使用真实世界数据（RWD）。与测量真实世界肿瘤学终点相关的挑战，如无进展生存期（PFS），是限制将ECA用作试验人群对照的使用和接受度的一个因素。在真实世界中疾病评估的方式和时间的差异可能会引入测量误差，并限制真实世界PFS（rwPFS）与试验无进展生存期的可比性。虽然在使用真实世界数据进行外部对照试验时，测量误差是一个已知的挑战，但描述关键促成因素的文献有限，尤其是在多发性骨髓瘤（MM）的背景下。我们区分了归因于终点如何推导或确定的偏差（错误分类偏差）和观察或评估结果的时间偏差（监测偏差）。我们进一步描述了多发性骨髓瘤RWD中进展事件的错误分类（即假阳性、假阴性）和不规则评估频率如何分别导致这些偏差。我们进行了一项模拟研究，以说明这些偏差单独和共同的表现方式。我们在模拟中观察到，某些类型的测量误差可能比其他误差对错误测量的中位PFS（mPFS）和真实mPFS之间的可比性产生更大的影响。例如，当观察到的进展事件被错误分类为假阳性或假阴性时，错误测量的mPFS可能分别偏向更早的时间（mPFS偏差=-6.4个月）或更晚的时间（mPFS偏差=13个月）。然而，当事件被正确分类但评估频率不规则时，错误测量的mPFS与真实mPFS更相似（mPFS偏差=0.67个月）。当错误分类的进展事件和不规则评估时间同时发生时，它们可能产生大于各部分之和的偏差。更好地理解终点测量误差以及由此产生的偏差如何在RWD中表现出来，对于在肿瘤学及其他领域稳健构建ECA非常重要。量化测量误差影响的模拟在规划ECA研究时会有所帮助，并可以在存在终点测量差异的情况下将结果置于背景中。
