Regression models for method comparison data

Graham Dunn

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Regression methods for the analysis of paired measurements produced by two fallible assay methods are described and their advantages and pitfalls discussed. The difficulties for the analysis, as in any errors-in-variables problem lies in the lack of identifiability of the model and the need to introduce questionable and often naïve assumptions in order to gain identifiability. Although not a panacea, the use of instrumental variables and associated instrumental variable (IV) regression methods in this area of application has great potential to improve the situation. Large samples are frequently needed and two-phase sampling methods are introduced to improve the efficiency of the IV estimators. Copyright © Taylor & Francis Group, LLC.
    Original languageEnglish
    Pages (from-to)739-756
    Number of pages17
    JournalJournal of Biopharmaceutical Statistics
    Volume17
    Issue number4
    DOIs
    Publication statusPublished - Jul 2007

    Keywords

    • Deming regression
    • Errors-in-variables
    • Instrumental variable
    • Method comparison

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