Predictive adjudication rate modeling for prioritizing case distribution in BICR for oncology clinical trials

Abstract

<strong>Purpose: </strong>Blinded independent central review is recommended by the US FDA for registration of oncology trials as it provides bias-free image assessment and avoidance of potential unblinding of patient data. Double read with adjudication is a highly advocated review model used in such trials. Disagreement between readers is natural and inevitable. Radiological disagreement rates or Adjudication Rate (AR) of 30–65% are reported by several papers since 1959 for different oncologic indications. The aim of the study is to develop and use an algorithm to identify reader pair with predicted high AR and investigate if overall study AR can be kept constant or improved by assigning less cases to a reader pair with high AR. <br/> <strong>Methods:</strong> A retrospective analysis was performed of 285 subjects with 3351 post-baseline timepoints reviewed by board-certified radiologist reviewers using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria in a BICR set up. The reader adjudication rate was calculated and analyzed throughout the duration of review. The distribution of cases per reader and distribution of cases per reader pair was calculated and overall study AR, and each reader pair AR were calculated. <br/> <strong>Data Analysis Methods:</strong> Data was prepared and analyzed using linear regression with MS Excel and R programming script (R version 4.1.2 (2021-11-01) -- "Bird Hippie" augmented by RStudio 2022.07.0+548 "Spotted Wakerobin". <br/> <strong>Results:</strong> Using the data at completion of 50% reads, predicted AR per reader pair was found to correlate well for five out of six reader pairs on the study. This predicted AR can then be used to assign more cases to reader pair with low AR and less cases to reader pair with high AR to keep study level variability low. <br/> <strong>Conclusions: </strong>Predictive modeling of AR using linear regression can provide an insight into variability of each reader pair, which in turn determines the AR of that reader pair and collectively determines study AR. However, it is still not clear to what level of prioritization in case assignment to specific pairs can be considered acceptable and not artificial.

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