SW-1B - Bridging Real-world and RCT Endpoints: Mitigating Measurement Error Bias in Time-to-event Endpoints in Studies using Oncology Real-world Data

Background: Guidance from regulators and various initiatives have been published to advance the use of oncology real-world data (RWD) to complement clinical trials, namely to: (a) contextualize results of single-arm trials, and (b) enhance understanding of efficacy when control arm regimens no longer reflect the standard of care. However, use of RWD in oncology is still limited due to concerns related to several biases, such as differences in how (misclassification bias) and when (surveillance bias) patient outcome data are ascertained in routine clinical practice compared to trials. These measurement biases limit comparability of real-world and trial endpoints, like progression-free survival (PFS).

Objectives: 1. To review sources of outcome measurement error that lead to surveillance and misclassification bias when comparing oncology trials with RWD, and to describe relevant attributes of RWD that contribute to them.
2. To present novel approaches to quantify misclassification and surveillance biases in outcomes ascertained from RWD via simulation.
3. To demonstrate newly developed statistical methods and quantitative bias analyses to address outcome measurement error, emphasizing practical data considerations when applying such techniques.

Who would benefit by attending: Researchers who design RWD studies for regulatory decision making, researchers interested in improving comparability between trial and real-world endpoints.

Description: Dr. Jennifer Lund will provide an overview of outcome measurement error and resulting biases in RWD, highlight how they manifest in externally controlled trial designs, and describe various attributes of RWD that contribute to these biases. (20 mins)

James Roose will present a simulation study to emulate RWD attributes that can cause outcome measurement error and quantify their impact on bias of real-world median PFS. (20 mins)

Dr. Benjamin Ackerman and Dr. Youyi Zhang will present novel methods to mitigate outcome measurement error when using RWD and highlight their performance via simulation and an illustrative application in multiple myeloma. They will highlight the relevance of these methods as quantitative bias analyses to contextualize RWD uncertainties. (20 mins)

Dr. Sebastian Schneeweiss will serve as a discussant, synthesizing and providing commentary on the first three presentations. (10 mins)

Dr. Craig Meyer will moderate a panel discussion about designing appropriate studies with fit for use RWD, with emphasis on applying the novel methods to address outcome measurement error alongside other key biases. Q+A from the audience will be included to promote participation from the public. Audience members are also encouraged to provide thoughts and commentary on the content. (20 mins)

Funding Disclaimer: This project is supported by the Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award [FAIN] totaling $468,058 (32 percent) funded by FDA/HHS and approximately $1,001,454 (68 percent) funded by non-government source(s). The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA/HHS, or the U.S. Government.