Grant Number:	1R03CA212799-01 Interpret this number
Primary Investigator:	Wang, Molin
Organization:	Harvard School Of Public Health
Project Title:	Statistical Methods for Analysis of Pooled Continuous Biomarker Data Arisen From Multiple Studies
Fiscal Year:	2017

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.




Spline Analysis of Biomarker Data Pooled from Multiple Matched/Nested Case-Control Studies.
Authors: Wu Y. , Gail M. , Smith-Warner S. , Ziegler R. , Wang M. .
Source: Cancers, 2022-06-03 00:00:00.0; 14(11), .
EPub date: 2022-06-03 00:00:00.0.
PMID: 35681763
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A repeated measures approach to pooled and calibrated biomarker data.
Authors: Sloan A. , Cheng C. , Rosner B. , Ziegler R.G. , Smith-Warner S.A. , Wang M. .
Source: Biometrics, 2021-12-30 00:00:00.0; , .
EPub date: 2021-12-30 00:00:00.0.
PMID: 34967001
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Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays.
Authors: Stopsack K.H. , Tyekucheva S. , Wang M. , Gerke T.A. , Vaselkiv J.B. , Penney K.L. , Kantoff P.W. , Finn S.P. , Fiorentino M. , Loda M. , et al. .
Source: Elife, 2021-12-23 00:00:00.0; 10, .
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PMID: 34939926
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Statistical methods for analysis of combined biomarker data from multiple nested case-control studies.
Authors: Cheng C. , Sloan A. , Wang M. .
Source: Statistical Methods In Medical Research, 2021 Aug; 30(8), p. 1944-1959.
EPub date: 2021-07-07 00:00:00.0.
PMID: 34232834
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STATISTICAL METHODS FOR ANALYSIS OF COMBINED CATEGORICAL BIOMARKER DATA FROM MULTIPLE STUDIES.
Authors: Cheng C. , Wang M. .
Source: The Annals Of Applied Statistics, 2020 Sep; 14(3), p. 1146-1163.
EPub date: 2020-09-18 00:00:00.0.
PMID: 33633815
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Statistical methods for biomarker data pooled from multiple nested case-control studies.
Authors: Sloan A. , Smith-Warner S.A. , Ziegler R.G. , Wang M. .
Source: Biostatistics (oxford, England), 2019-11-21 00:00:00.0; , .
EPub date: 2019-11-21 00:00:00.0.
PMID: 31750898
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Design and analysis considerations for combining data from multiple biomarker studies.
Authors: Sloan A. , Song Y. , Gail M.H. , Betensky R. , Rosner B. , Ziegler R.G. , Smith-Warner S.A. , Wang M. .
Source: Statistics In Medicine, 2019-04-15 00:00:00.0; 38(8), p. 1303-1320.
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