Analysis of Left-Censored Multiplex Immunoassay Data: A Unified Approach

• Activity Number: 321
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Elizabeth Hill*+ and Elizabeth Slate
• Companies: Medical University of South Carolina and Florida State University
• Keywords: Multiplex Immunoassays ; Censored data ; Bayesian hierarchical models ; Variance function ; Biomarker discovery ; Head and neck cancer

Abstract

Multiplex immunoassays (MIAs) are moderate- to high-throughput platforms for simultaneous quantitation of a panel of analytes, and increasingly are popular as hypothesis generating tools for targeted biomarker identification. As such, MIAs are not always rigorously validated, and often little is known about analytes' expected concentrations in samples derived from the target population. As a consequence, MIA data can be plagued by high proportions of concentrations flagged either as 'out-of-range' - samples for which the observed response falls below (above) the lower (upper) asymptote of a 5-parameter logistic calibration curve - or as extrapolated beyond the smallest or largest standard. We present a unified approach to the analysis of left-censored MIA data in the context of a Bayesian hierarchical model that incorporates background estimation, standard curve fitting, and modeling of observed fluorescence as a function of unobserved (latent) analyte concentration, with accommodation of left-censored concentrations via variance function specification. We present results from both a simulation study and cytokine array analysis of serum specimens from head and neck cancer patients.