Comparing and Combining Data Across Multiple Sources via Integration of Paired-Sample Data to Correct for Measurement Error

• Activity Number: 341
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: W.J. Youden Award in Interlaboratory Testing
• Author(s): Yunda Huang* and Ying Huang and Zoe Moodie and Sue Li and Steve Self
• Companies: and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
• Keywords: assay comparison ; inter-laboratory measurement error ; multiple data sources ; regression calibration

Abstract

This paper describes a statistical method to carry out inter-lab measurement adjustment in comparing and combining independent samples from different labs, via integration of external data collected on paired samples from the same two labs. We propose: 1) normalization of individual level data from two labs to the same scale via the expectation of true measurements conditioning on the observed; 2) comparison of mean assay values between two independent samples in the Main study accounting for inter-lab measurement error; and 3) sample size calculations of the paired-sample study so that hypothesis testing error rates are appropriately controlled in the Main study comparison. Because the goal is not to estimate the true underlying measurements but to combine data on the same scale, our proposed methods do not require that the true values for the error-prone measurements are known in the external data. Simulation results under a variety of scenarios demonstrate satisfactory finite sample performance of our proposed methods when measurement errors vary. We illustrate our methods using real ELISpot assay data generated by two HIV vaccine laboratories.