Dr. Anitha Singareddy Profile

Dr. Anitha Singareddy

at PAREXEL International Corp

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Publications (4)

Proceedings Article | 16 March 2020 Presentation + Paper

Using “Reader Disagreement Index” as a predictive reviewer performance monitoring tool for timely intervention

Manish Sharma, J. Michael O'Connor, Anitha Singareddy, Madhuri Madasu, Nicholas Enus, Kassel Fotinos-Hoyer, Yibin Shao

Proceedings Volume 11316, 113160A (2020) https://doi.org/10.1117/12.2549980

KEYWORDS: Oncology, Clinical trials, Statistical analysis, Analytical research

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Purpose: Blinded Independent Central Review (BICR) is a well-accepted method employed in many oncology registration trials. Ongoing monitoring radiologist “reader” performance is both good clinical trial practice and a requirement by regulatory authorities. We continue to use Reader Disagreement Index (RDI) as an important measure in BICR. In this work we studied RDI as an early indicator to identify an outlier reader during the monitoring of reader performance in BICR. Early indication would enable early intervention and thus possibly improve trial outcomes.
Methods: We performed a retrospective analysis of readers’ RDIs in nineteen different clinical trials. Ninety-two reader performances were examined at five intervals in each trial. These individual trial reviews were conducted by forty-three board-certified radiologist readers using several established imaging assessment trial criteria. The objective was to see how well RDI performance above a threshold at progressive monitoring intervals would “flag” a potential overall end-point performance “issue” for that specific reader.
Results: We present results for the prediction of exceeding threshold (one standard deviation above a study mean RDI). Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were determined for the predicted performance outcomes. We explored interpreting multiple “flags” for each trial to improve the aforementioned metrics.
Conclusions: One would expect that a “flag” of RDI exceeding threshold at an early-stage would likely give a useful prediction of end-point reader performance. We refined our methods to use multiple flags which enable statistically improved Specificity and PPV. Improved predictive capability at early stage intervals coupled with persistent monitoring across subsequent intervals will enable trial managers to focus on specific readers. An earlier indication of possible reader performance issues can permit proactive intervention and enhance good trial practices.

Proceedings Article | 4 March 2019 Presentation + Paper

Reader disagreement index: a better measure of overall review quality monitoring in an oncology trial compared to adjudication rate

Manish Sharma, J. Michael O'Connor, Anitha Singareddy

Proceedings Volume 10952, 109520Q (2019) https://doi.org/10.1117/12.2512611

KEYWORDS: Oncology, Clinical trials, Tumors, Analytical research, Image registration, Tumor growth modeling, Data acquisition, Image acquisition, Performance modeling

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Proceedings Article | 4 March 2019 Presentation + Paper

Development of methods to evaluate probability of reviewer’s assessment bias in Blinded Independent Central Review (BICR) imaging studies

J. Michael O'Connor, Manish Sharma, Anitha Singareddy

Proceedings Volume 10952, 109520P (2019) https://doi.org/10.1117/12.2512603

KEYWORDS: Clinical trials, Statistical analysis, Analytical research, Solids, Tumors, Image processing, Matrices, Data analysis, Clinical research, Computer programming

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Proceedings Article | 10 March 2017 Presentation + Paper

Development of discordant likelihood metrics for use in evaluation of 'double reviewer' performance in blinded independent central review (BICR) RECIST imaging studies

J. Michael O'Connor, Manish Sharma, Anitha Singareddy, Annette Schmid

Proceedings Volume 10136, 1013609 (2017) https://doi.org/10.1117/12.2255753

KEYWORDS: Diagnostics, Data analysis, Analytical research, Clinical research, Performance modeling, Image analysis, Clinical trials, Tumors, Matrices, Chromium, Statistical analysis, Oncology

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