Grant Details
Grant Number: |
5R01CA269398-03 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: |
2024 |
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
Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.
Authors: Sun Y.
, Salerno S.
, Pan Z.
, Yang E.
, Sujimongkol C.
, Song J.
, Wang X.
, Han P.
, Zeng D.
, Kang J.
, et al.
.
Source: Harvard Data Science Review, 2024 Winter; 6(1), .
EPub date: 2024-01-31 00:00:00.0.
PMID: 38974963
Related Citations
Causal meta-analysis by integrating multiple observational studies with multivariate outcomes.
Authors: Guha S.
, Li Y.
.
Source: Biometrics, 2024-07-01 00:00:00.0; 80(3), .
PMID: 39073772
Related Citations
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 00:00:00.0.
PMID: 38106966
Related Citations
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, 2023-07-17 00:00:00.0; , .
EPub date: 2023-07-17 00:00:00.0.
PMID: 37459178
Related Citations
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 00:00:00.0; 13(1), p. 7318.
EPub date: 2023-05-05 00:00:00.0.
PMID: 37147440
Related Citations
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 00:00:00.0; 6(5), p. e2311966.
EPub date: 2023-05-01 00:00:00.0.
PMID: 37145597
Related Citations
Asynchronous and error-prone longitudinal data analysis via functional calibration.
Authors: Chang X.
, Li Y.
, Li Y.
.
Source: Biometrics, 2023-04-12 00:00:00.0; , .
EPub date: 2023-04-12 00:00:00.0.
PMID: 37042741
Related Citations
Traction Bronchiectasis/Bronchiolectasis in Interstitial Lung Abnormality: Follow-up in the COPDGene.
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.
, et al.
.
Source: American Journal Of Respiratory And Critical Care Medicine, 2023-03-10 00:00:00.0; , .
EPub date: 2023-03-10 00:00:00.0.
PMID: 36898128
Related Citations
OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations.
Authors: Pan Z.
, Zhang R.
, Shen S.
, Lin Y.
, Zhang L.
, Wang X.
, Ye Q.
, Wang X.
, Chen J.
, Zhao Y.
, et al.
.
Source: Ebiomedicine, 2023 Feb; 88, p. 104443.
EPub date: 2023-01-24 00:00:00.0.
PMID: 36701900
Related Citations
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 09; 171, p. 115-120.
EPub date: 2022-08-01 00:00:00.0.
PMID: 35939954
Related Citations
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 09; 171, p. 115-120.
EPub date: 2022-08-01 00:00:00.0.
PMID: 35939954
Related Citations