Integrated Partially Linear Model for Multi-Centre Studies with Heterogeneity and Batch Effect in Covariates

Details

• Personal Author:
  Shao Y ; Yang L
• Description:
  Multi-centre study is increasingly used for borrowing strength from multiple research groups to obtain reproducible study findings. Regression analysis is widely used for analysing multi-group studies, however, some of the regression predictors are nonlinear and/or often measured with batch effects. Also, the group compositions are potentially heterogeneous across different centres. The conventional pooled data analysis can cause biased regression estimates. This paper proposes an integrated partially linear regression model (IPLM) to account for predictor's nonlinearity, general batch effect, group composition heterogeneity, and potential measurement-error in covariates simultaneously. A local linear regression-based approach is employed to estimate the nonlinear component and a regularization procedure is introduced to identify the predictors' effects. The IPLM-based method has estimation consistency and variable-selection consistency. Moreover, it has a fast computing algorithm and its effectiveness is supported by simulation studies. A multi-centre Alzheimer's disease research project is provided to illustrate the proposed IPLM-based analysis. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Author Keywords: Multi-centre Study Data Harmonization General Batch Effects Group Composition Heterogeneity Mathematical Models Partially Linear Regression Model Simulation Methods Statistical Analysis Statistical Quality Control
• ISSN:
  0233-1888
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  57
• Issue:
  5
• NIOSHTIC Number:
  nn:20069241
• Citation:
  Statistics 2023 Sep; 57(5):987-1009
• Contact Point Address:
  Yongzhao Shao, Department of Population Health, New York University, New York, NY 10012-1126, USA
• Email:
  shaoy01@nyu.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  New York University School of Medicine
• Peer Reviewed:
  True
• Start Date:
  20220701
• Source Full Name:
  Statistics
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:3d3ae00ae03ee960b97908c804daf3b245f2884af161e41b6e6adceaa93613205a29d6b262439884c418ac0d79047f512dcdb300feb7d488f00a6ffc717fb2da
• Download URL:
  https://stacks.cdc.gov/view/cdc/207750/cdc_207750_DS1.pdf
• File Type:
  [PDF - 2.43 MB ]