Skip to main content
Grant Details

Grant Number: 5R03CA235122-02 Interpret this number
Primary Investigator: Wang, Ching-Yun
Organization: Fred Hutchinson Cancer Research Center
Project Title: Novel Methods for Missing Subtype Data in Colorectal Cancer
Fiscal Year: 2020


Abstract

Project Summary/Abstract The proposed project is in response to PAR-18-021, NCI Small Grants Program for Cancer Research (NCI Omnibus R03). We are motivated by the resources in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). We are primarily interested in developing and applying innovative statistical methods for missing cancer subtype data. Many diseases, such as colorectal cancer (CRC), are heterogeneous. Molecular characterization of tumors has provided evidence of multiple tumor subtypes that develop through activation of diverse neoplastic pathways. Important CRC tumor subtypes include microsatellite instability (MSI) status, somatic mutations in BRAF and KRAS, and CpG island methylator phenotype. For instance, MSI status is associated with survival outcomes and treatment response. However, some individuals may have unknown MSI status, and other tumor biomarkers. Regression analysis may encounter a challenge due to missing data in some individuals of the study cohort. Methodology for missing data is often required to address the issue on bias in effect estimation and efficiency. Specific aims of this proposal include: (i) To develop and apply methods to take into account missing cancer subtype data in multinomial logistic regression. (ii) To develop and apply methods to adjust for survival analysis among cancer cases in which multiple tumor biomarkers may be missing. The methods developed in the proposal are applicable to the GECCO and other studies where cancer subtype data may be unknown among some study individuals. The methods can be applied to other common study designs such as nested case-control designs and Cox regression with competing risks.



Publications

Robust best linear weighted estimator with missing covariates in survival analysis.
Authors: Wang C.Y. , Hsu L. , Harrison T. .
Source: Statistics in medicine, 2024-02-25; , .
EPub date: 2024-02-25.
PMID: 38402690
Related Citations

A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error-contaminated continuous time-dependent exposure.
Authors: Song X. , Chao E.C. , Wang C.Y. .
Source: Biometrics, 2023 Mar; 79(1), p. 437-448.
EPub date: 2021-11-15.
PMID: 34694632
Related Citations

A Flexible Method for Diagnostic Accuracy with Biomarker Measurement Error.
Authors: Wang C.Y. , Feng Z. .
Source: Mathematics (Basel, Switzerland), 2023-02-01; 11(3), .
EPub date: 2023-01-19.
PMID: 37251695
Related Citations

Population Size Estimation using Zero-truncated Poisson Regression with Measurement Error.
Authors: Hwang W.H. , Stoklosa J. , Wang C.Y. .
Source: Journal of agricultural, biological, and environmental statistics, 2022 Jun; 27(2), p. 303-320.
EPub date: 2022-01-12.
PMID: 35813491
Related Citations

Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard.
Authors: Wang C.Y. , Song X. .
Source: Biometrics, 2021 Jun; 77(2), p. 561-572.
EPub date: 2020-06-25.
PMID: 32557567
Related Citations

Multinomial logistic regression with missing outcome data: An application to cancer subtypes.
Authors: Wang C.Y. , Hsu L. .
Source: Statistics in medicine, 2020-10-30; 39(24), p. 3299-3312.
EPub date: 2020-07-06.
PMID: 32628308
Related Citations

Methods for generalized change-point models: with applications to human immunodeficiency virus surveillance and diabetes data.
Authors: Tapsoba J.D. , Wang C.Y. , Zangeneh S. , Chen Y.Q. .
Source: Statistics in medicine, 2020-04-15; 39(8), p. 1167-1182.
EPub date: 2020-01-29.
PMID: 31997385
Related Citations




Back to Top