Quantitative bias analyses to address measurement error in time-to-event endpoints

Benjamin AckermanJohnson & Johnson (United States)

Ryan W. GanJohnson & Johnson (United States)

Youyi ZhangJohnson & Johnson (United States)

Jennifer HaydenJohnson & Johnson (United States)

Jocelyn R. WangJohnson & Johnson (United States)

Craig S MeyerJohnson & Johnson (United States)

Juned SiddiqueNorthwestern University

Jennifer L. LundUniversity of North Carolina at Chapel Hill

Janick WeberpalsBrigham and Women's Hospital

S SchneeweissBrigham and Women's Hospital

American Journal of Epidemiology·February 4, 2026

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Abstract

When using real-world data to construct an external comparator arm for a single-arm trial, there may be differences in how and when patients are assessed for disease between trial and real-world settings. Such differences can generate outcome measurement error when comparing time to event endpoints and lead to biased findings. Recent methods have been developed to mitigate measurement error bias in real-world endpoints; however, they rely on the existence of a validation sample, ie, data on a set of patients where both the “true” trial-like and “mis-measured” real-world measures are collected. We demonstrate how novel statistical methods can be leveraged as quantitative bias analyses (QBA) to contextualize real-world evidence findings when outcome measurement error is of concern, but validation samples are infeasible to collect. QBA allows researchers to set plausible ranges for the amount of error when not directly measurable. We highlight how to conduct QBA with two recent methods, Cumulative Incidence Curve Correction and Survival Regression Calibration, and illustrate how to generate plausible parameter values through simulation. We provide an illustrative QBA example in a cohort of real-world patients with Newly Diagnosed Multiple Myeloma and provide practical guidance to apply QBA for outcome measurement error and interpret results.

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