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Grant Details

Grant Number: 4R37CA218413-06 Interpret this number
Primary Investigator: Bansal, Aasthaa
Organization: University Of Washington
Project Title: Personalized Risk-Adaptive Surveillance (PRAISE) - Implications of Algorithmic Bias
Fiscal Year: 2023


ABSTRACT Algorithmic bias is an emerging and highly relevant topic in health policy that draws attention to the idea that seemingly well-performing predictive algorithms built using biased data can propagate systemic biases and disparities existing in clinical practice. Our primary goal in the parent grant was to innovatively use machine learning based risk prediction and value-of-information methodology to develop a personalized risk-adaptive surveillance (PRAISE) framework and assess its impact on outcomes, including costs, time to recurrence and survival, for colorectal cancer survivors who have been treated for their primary cancer and are under surveillance for recurrence. Decision-making in the PRAISE framework is driven by dynamic risk predictions for recurrence, which identify high-risk patients to target for more frequent surveillance testing. However, potential sources of racial/ethnic disparities along the cancer care continuum from diagnosis to survivorship can lead to distorted risk predictions during surveillance. Using distorted risk predictions can have implications for decision-making, potentially propagating and exacerbating biases that exist in clinical practice and resulting in poorer outcomes for certain subgroups. The overarching goal of the proposed research is to understand and address algorithmic bias in the PRAISE framework. Specifically, we will first characterize heterogeneity in current practice with respect to surveillance testing patterns, recurrence detection and survival across racial/ethnic subgroups for patients diagnosed with and treated for colorectal cancer (Aim 1). This will help us better understand the sources of bias in our data and will better inform our approach in Aim 2, where we will use emerging and novel methods to mitigate racial/ethnic bias in our previously developed dynamic risk prediction model for colorectal cancer recurrence. Finally, in Aim 3, we will develop an outcomes-based framework to assess the implications of using a biased versus a bias-corrected risk prediction model to guide surveillance testing among colorectal cancer survivors, specifically through their effect on decision-making and subgroup-specific health and cost outcomes. This important work will motivate the use of new methods for addressing bias in risk prediction models in cancer and other clinical areas.


Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence.
Authors: Rodriguez P.J. , Heagerty P.J. , Clark S. , Khor S. , Chen Y. , Haupt E. , Hahn E.E. , Shankaran V. , Bansal A. .
Source: JCO clinical cancer informatics, 2023 Sep; 7, p. e2300066.
PMID: 37963310
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Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors.
Authors: Khor S. , Haupt E.C. , Hahn E.E. , Lyons L.J.L. , Shankaran V. , Bansal A. .
Source: JAMA network open, 2023-06-01; 6(6), p. e2318495.
EPub date: 2023-06-01.
PMID: 37318804
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Underutilization or appropriate care? Assessing adjuvant chemotherapy use and survival in 3 heterogenous subpopulations with stage II/III colorectal cancer within a large integrated health system.
Authors: Chen Y. , Shankaran V. , Hahn E.E. , Haupt E.C. , Bansal A. .
Source: Journal of managed care & specialty pharmacy, 2023 Jun; 29(6), p. 635-646.
PMID: 37276035
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Racial Disparities in the Ascertainment of Cancer Recurrence in Electronic Health Records.
Authors: Khor S. , Heagerty P.J. , Basu A. , Haupt E.C. , Lyons L.J.L. , Hahn E.E. , Bansal A. .
Source: JCO clinical cancer informatics, 2023 Jun; 7, p. e2300004.
PMID: 37267516
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Development and Internal Validation of a Prognostic Model of the Probability of Death or Lung Transplantation Within 2 Years for Patients With Cystic Fibrosis and FEV1 ≤ 50% Predicted.
Authors: Ramos K.J. , Hee Wai T. , Stephenson A.L. , Sykes J. , Stanojevic S. , Rodriguez P.J. , Bansal A. , Mayer-Hamblett N. , Goss C.H. , Kapnadak S.G. .
Source: Chest, 2022 Oct; 162(4), p. 757-767.
EPub date: 2022-05-26.
PMID: 35643116
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Assessing surveillance utilization and value in commercially insured patients with colorectal cancer.
Authors: Suh K. , Shankaran V. , Bansal A. .
Source: The American journal of managed care, 2022-05-01; 28(5), p. e163-e169.
EPub date: 2022-05-01.
PMID: 35546589
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A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing.
Authors: Bansal A. , Heagerty P.J. , Inoue L.Y.T. , Veenstra D.L. , Wolock C.J. , Basu A. .
Source: Medical decision making : an international journal of the Society for Medical Decision Making, 2022 May; 42(4), p. 474-486.
EPub date: 2021-11-07.
PMID: 34747265
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A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.
Authors: Rodriguez P.J. , Veenstra D.L. , Heagerty P.J. , Goss C.H. , Ramos K.J. , Bansal A. .
Source: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2022 Mar; 25(3), p. 350-358.
EPub date: 2021-12-22.
PMID: 35227445
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Predictors of tyrosine kinase inhibitor adherence trajectories in patients with newly diagnosed chronic myeloid leukemia.
Authors: Clark S.E. , Marcum Z.A. , Radich J.P. , Bansal A. .
Source: Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners, 2021 Dec; 27(8), p. 1842-1852.
EPub date: 2020-11-11.
PMID: 33175653
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Bias in Mean Survival From Fitting Cure Models With Limited Follow-Up.
Authors: Othus M. , Bansal A. , Erba H. , Ramsey S. .
Source: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2020 Aug; 23(8), p. 1034-1039.
EPub date: 2020-07-31.
PMID: 32828215
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Patterns of Surveillance Advanced Imaging and Serum Tumor Biomarker Testing Following Launch of the Choosing Wisely Initiative.
Authors: Miles R.C. , Lee C.I. , Sun Q. , Bansal A. , Lyman G.H. , Specht J.M. , Fedorenko C.R. , Greenwood-Hickman M.A. , Ramsey S.D. , Lee J.M. .
Source: Journal of the National Comprehensive Cancer Network : JNCCN, 2019-07-01; 17(7), p. 813-820.
PMID: 31319393
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A Novel Tool to Evaluate the Accuracy of Predicting Survival and Guiding Lung Transplantation in Cystic Fibrosis.
Authors: Bansal A. , Mayer-Hamblett N. , Goss C.H. , Chan L.N. , Heagerty P.J. .
Source: Epidemiology (Sunnyvale, Calif.), 2019; 9(2), .
EPub date: 2019-06-17.
PMID: 31523488
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A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes.
Authors: Bansal A. , Heagerty P.J. .
Source: Diagnostic and prognostic research, 2019; 3, p. 14.
EPub date: 2019-07-25.
PMID: 31367681
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A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making.
Authors: Bansal A. , Heagerty P.J. .
Source: Medical decision making : an international journal of the Society for Medical Decision Making, 2018 Nov; 38(8), p. 904-916.
EPub date: 2018-10-14.
PMID: 30319014
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