Skip to main content

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.

The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit cc.nih.gov.

Updates regarding government operating status and resumption of normal operations can be found at opm.gov.

An official website of the United States government
Grant Details

Grant Number: 5R03CA212799-02 Interpret this number
Primary Investigator: Wang, Molin
Organization: Harvard University D/B/A Harvard School Of Public Health
Project Title: Statistical Methods for Analysis of Pooled Continuous Biomarker Data Arisen From Multiple Studies
Fiscal Year: 2018


Abstract

Project Summary / Abstract Increasingly, multiple studies relating biomarkers to cancer and other health outcomes are pooled to obtain an overall risk profile, and a major challenge of pooling biomarker data is potential sources of variability of the biomarker data, including assay and laboratory variability. Currently there are no reliable and well-evaluated statistical methods to conduct the aggregated analysis for pooled biomarker data while taking care of the calibration process that correct for the between-study biomarker variability. In this proposal, we will develop efficient statistical methods for incorporating the calibration process in the aggregated data analysis. User- friendly software implementing the methods will be made publicly available. In addition, analysis results have potential to be substantially different between using the two commonly used methods for analyzing pooled data, the two-stage analysis method and the aggregated data analysis method, and in the two-stage method, between the fixed effect model method and the random effect model method. Investigators conducting consortial research are confronted with the choice between the methods. We will compare these methods such that the choices of analysis methods will be made to exploit the full power of the data available to maximize the information gained, while at the same time only making minimum and realistic assumptions.



Publications

Error Notice

The database may currently be offline for maintenance and should be operational soon. If not, we have been notified of this error and will be reviewing it shortly.

We apologize for the inconvenience.
- The DCCPS Team.

Back to Top