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Grant Details

Grant Number: 5R01CA269398-02 Interpret this number
Primary Investigator: Li, Yi
Organization: University Of Michigan At Ann Arbor
Project Title: Detecting Racial Disparities in Cancer Survival By Integrating Multiple High-Dimensional Observational Studies
Fiscal Year: 2023


Abstract

PROJECT SUMMARY/ABSTRACT Despite overall improvements, ethnic or racial disparities continue to increase, suggesting deficiencies in research designs for understanding disparities. For example, compared to the 2017 US Census, most observational cancer studies were found to over represent Caucasians and underrepresent African Americans and Asians. How to utilize these studies to detect and understand racial disparities remains challenging. This proposal is motivated by the Boston Lung Cancer Survival Cohort (BLCSC), one of the largest lung cancer cohorts globally, which consists of lung cancer cases registered since 1992 at the Dana-Farber Cancer Institute (DFCI) and the Massachusetts General Hospital (MGH), and has expanded to the MD Anderson Cancer Center (MDACC) and Mayo Clinic. This rich database provides a unique opportunity for studying racial disparities in cancer outcomes as well as presents a challenge with unbalanced covariates across racial groups. We also have access to the International Lung and Cancer Consortium (ILCCO), an international cohort established in 2004 with a data structure similar to BLCSC. Leveraging these cancer cohorts, we develop methods with a common goal of effectively identifying racial disparities in cancer outcomes by integrating high dimensional observational studies with multiple racial groups. Rich datasets like BLCSC and ILLCO are ideal for integrative, unconfounded detection of racial disparities in cancer outcomes, and for generating statistical findings generalizable to a realistic and inclusive larger population.



Publications

Debiased lasso for stratified Cox models with application to the national kidney transplant data.
Authors: Xia L. , Nan B. , Li Y. .
Source: The annals of applied statistics, 2023 Dec; 17(4), p. 3550-3569.
EPub date: 2023-10-30.
PMID: 38106966
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Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach.
Authors: Yang Y. , Pan Z. , Kang J. , Brummett C. , Li Y. .
Source: Biometrics, 2023 Dec; 79(4), p. 3388-3401.
EPub date: 2023-07-17.
PMID: 37459178
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Asynchronous and error-prone longitudinal data analysis via functional calibration.
Authors: Chang X. , Li Y. , Li Y. .
Source: Biometrics, 2023 Dec; 79(4), p. 3374-3387.
EPub date: 2023-05-07.
PMID: 37042741
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Traction Bronchiectasis/Bronchiolectasis in Interstitial Lung Abnormality: Follow-up in the COPDGene Study.
Authors: Hata A. , Hino T. , Li Y. , Johkoh T. , Christiani D.C. , Lynch D.A. , Cho M.H. , Silverman E.K. , Hunninghake G.M. , Hatabu H. .
Source: American journal of respiratory and critical care medicine, 2023-05-15; 207(10), p. 1395-1398.
PMID: 36898128
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Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.
Authors: Sun Y. , Salerno S. , He X. , Pan Z. , Yang E. , Sujimongkol C. , Song J. , Wang X. , Han P. , Kang J. , et al. .
Source: Scientific reports, 2023-05-05; 13(1), p. 7318.
EPub date: 2023-05-05.
PMID: 37147440
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Prediagnosis Smoking Cessation and Overall Survival Among Patients With Non-Small Cell Lung Cancer.
Authors: Wang X. , Romero-Gutierrez C.W. , Kothari J. , Shafer A. , Li Y. , Christiani D.C. .
Source: JAMA network open, 2023-05-01; 6(5), p. e2311966.
EPub date: 2023-05-01.
PMID: 37145597
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Sex disparities in lung cancer survival rates based on screening status.
Authors: Rodriguez Alvarez A.A. , Yuming S. , Kothari J. , Digumarthy S.R. , Byrne N.M. , Li Y. , Christiani D.C. .
Source: Lung cancer (Amsterdam, Netherlands), 2022 Sep; 171, p. 115-120.
EPub date: 2022-08-01.
PMID: 35939954
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