Grant Details
Grant Number: |
5R03CA130089-02 Interpret this number |
Primary Investigator: |
Hsu, Chiu-Hsieh |
Organization: |
University Of Arizona |
Project Title: |
Statistical Methods for Colorectal Neoplastic Prevention Trials |
Fiscal Year: |
2010 |
Abstract
DESCRIPTION (provided by applicant): Colorectal cancer is one of the most common malignancies in the United States. There are an increasing number of studies using recurrent colorectal adenomas to evaluate the prevention effect for some promising agents. The number of recurrent colorectal adenomas is often measured by performing colonoscopy, which is known to miss a small percentage of existing adenomas and result in misclassification on recurrence status. In addition, some participants might not comply with the schedule of follow-up colonoscopy, which is scheduled to be performed once at the end of the study and, therefore, have variable followup lengths compared to the compliant participants. The reasons that a participant cannot comply with the schedule of follow-up colonoscopy could be informative of risk of recurrence and then bias the results derived from statistical methods that do not adjust for noncompliance. Conventional statistical methods for colorectal adenoma prevention trials cannot simultaneously incorporate misclassification and variable follow-up into analysis and cannot adjust for informative non-compliance without strong assumptions and, furthermore, may incorrectly produce equivocal results for some promising nutritional or chemopreventive agents.
The purpose of this application is to develop sophisticated and appropriate statistical models to describe the relationship between the preventive agents and recurrence of colorectal adenomas. We will use a latent variable recurrence model, which assumes a portion of non-recurrent participants were misclassified due missing existing adenomas at follow-up colonoscopy, to handle misclassification (Aim 1) and a weight function to incorporate the length of follow-up into analysis (Aim 2). The prognostic factors for risk of recurrence can be incorporated into the weight function to adjust for potential informative non-compliance. If the research in this application is successful, a better understanding of the relationship between preventive agents and recurrence of colorectal adenomas can be obtained and will then allow clinical investigators to identify an agent that truly reduces recurrence of colorectal adenomas.
Publications
Doubly Robust Nonparametric Multiple Imputation For Ignorable Missing Data
Authors: Long Q.
, Hsu C.H.
, Li Y.
.
Source: Statistica Sinica, 2012; 22, p. 149-172.
PMID: 22347786
Related Citations
Nonparametric Multiple Imputation For Receiver Operating Characteristics Analysis When Some Biomarker Values Are Missing At Random
Authors: Long Q.
, Zhang X.
, Hsu C.H.
.
Source: Statistics In Medicine, 2011-11-20 00:00:00.0; 30(26), p. 3149-61.
PMID: 22025311
Related Citations
Estimation Of Recurrence Of Colorectal Adenomas With Dependent Censoring Using Weighted Logistic Regression
Authors: Hsu C.H.
, Li Y.
, Long Q.
, Zhao Q.
, Lance P.
.
Source: Plos One, 2011; 6(10), p. e25141.
PMID: 22065985
Related Citations
Estimation Of Colorectal Adenoma Recurrence With Dependent Censoring
Authors: Hsu C.H.
, Long Q.
, Alberts D.S.
.
Source: Bmc Medical Research Methodology, 2009; 9, p. 66.
PMID: 19788750
Related Citations