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Statistical methods for biomarker data pooled from multiple nested case-control studies.

Abigail Sloan1, Stephanie A Smith-Warner2,3, Regina G Ziegler4

  • 1Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Biostatistics (Oxford, England)
|November 22, 2019
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Summary
This summary is machine-generated.

Pooling biomarker data requires calibration due to lab variability. The full calibration method minimizes bias in biomarker-disease association estimates, outperforming other approaches in nested case-control studies.

Keywords:
AggregationCalibrationConditional logistic regressionNested case–control studyPooling

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Pooling biomarker data across studies enhances statistical power and precision for disease association analyses.
  • Assay and laboratory variability necessitate biomarker data calibration before pooling.
  • Nested case-control studies often lack a universal reference assay for all participants.

Purpose of the Study:

  • To propose and evaluate methods for calibrating and combining biomarker data from multiple nested case-control studies.
  • To assess the performance of different calibration methods in estimating biomarker-disease associations and effect modification.
  • To identify the optimal calibration strategy for pooled biomarker analyses.

Main Methods:

  • Described a two-stage calibration method and two aggregated methods: internalized and full calibration.
  • Internalized method uses reference measurements when available, otherwise uses calibration models.
  • Full calibration method applies calibrated measurements to all subjects, while the two-stage method involves study-specific analyses followed by meta-analysis.

Main Results:

  • The full calibration method is preferred for aggregated approaches to minimize bias in point estimates.
  • Two-stage and full calibration methods yield similar effect and variance estimates, slightly larger than the internalized approach.
  • Demonstrated application using vitamin D levels, stroke risk, and body mass index modification in cardiovascular disease.

Conclusions:

  • The full calibration method offers a robust approach for pooling biomarker data in nested case-control studies.
  • Calibration is crucial for accurate biomarker-disease association estimates in multi-study analyses.
  • Findings provide practical guidance for researchers conducting pooled biomarker studies.