Online Program Home
My Program

Abstract Details

Activity Number: 179 - Emerging Methods for Complex Biomedical Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #327115
Title: Design and Analysis Considerations for Studies Involving Pooled Biomarker Data
Author(s): Abigail Sloan* and Molin Wang and Mitchell H. Gail
Companies: Harvard T.H. Chan School of Public Health and Harvard T.H. Chan School of Public Health and Division of Cancer Epidemiology and Genetics, NCI, NIH
Keywords: Calibration; Aggregation; Two-stage; Pooling; Biomarker
Abstract:

Pooling data from multiple studies improves estimation of exposure-disease associations through increased sample size. However, biomarker measurements can vary substantially across labs and often require calibration to a reference assay prior to pooling. We develop two statistical methods for aggregating biomarker data from multiple studies, the full calibration method and internalized method. The full calibration method calibrates all biomarker measurements regardless of reference lab measurement availability while the internalized method calibrates only non-reference lab measurements. We compare the performance of these aggregation methods to two-stage methods. Furthermore, we compare the aggregated and two-stage methods when estimating the calibration curve from controls only or from a random sample of individuals from the study cohort. Our findings include: (1) The preferred aggregated method depends on calibration study type. (2) The two-stage approaches produce similar average effect estimates to the preferred aggregated method, but those estimates have a larger MSE. We illustrate the methods in an application evaluating stroke risk in a pooling project of cohort studies.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program