The Structural Error-in-Equation Model to Evaluate Individual Bioequivalence

Jover, Lluís

doi:10.1002/bimj.200410151

Cited by 4 publications

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Review on Assessing Agreement

Barnhart

2018

Wiley StatsRef: Statistics Reference Online

View full text Add to dashboard Cite

Measurements serve as the basis for evaluation in almost all scientific disciplines, especially in physical sciences, medical studies, and health care. Issues related to reliable and accurate measurement have evolved over many decades. Requiring a measurement to be identical to the truth is sometimes impractical or impossible either because (i) the truth is simply not available or is measured with some error or (ii) some tolerable error is acceptable. Concepts of agreement, including reproducibility or reliability, are often used to determine whether the measurements can be used or not for evaluation. There has been substantial statistical literature in the last several decades on assessing agreement. This article provides a critical and comprehensive review on agreement concepts and their corresponding agreement indices developed for assessing agreement among measurements made on the same subject or experimental unit. The emphasis is on the intuitive understanding of concepts and on insights into both controversies and appropriate applications for continuous and categorical data. Four examples with either continuous or categorical measurements are used for illustration and discussion.

show abstract

A Review on Assessing Agreement

Barnhart

2018

Wiley StatsRef: Statistics Reference Online

View full text Add to dashboard Cite

show abstract

An Overview on Assessing Agreement with Continuous Measurements

Barnhart

Haber

2007

Journal of Biopharmaceutical Statistics

286

259

View full text Add to dashboard Cite

Reliable and accurate measurements serve as the basis for evaluation in many scientific disciplines. Issues related to reliable and accurate measurement have evolved over many decades, dating back to the nineteenth century and the pioneering work of Galton (1886), Pearson (1896, 1899, 1901), and Fisher (1925). Requiring a new measurement to be identical to the truth is often impractical, either because (1) we are willing to accept a measurement up to some tolerable (or acceptable) error, or (2) the truth is simply not available to us, either because it is not measurable or is only measurable with some degree of error. To deal with issues related to both (1) and (2), a number of concepts, methods, and theories have been developed in various disciplines. Some of these concepts have been used across disciplines, while others have been limited to a particular field but may have potential uses in other disciplines. In this paper, we elucidate and contrast fundamental concepts employed in different disciplines and unite these concepts into one common theme: assessing closeness (agreement) of observations. We focus on assessing agreement with continuous measurements and classify different statistical approaches as (1) descriptive tools; (2) unscaled summary indices based on absolute differences of measurements; and (3) scaled summary indices attaining values between -1 and 1 for various data structures, and for cases with and without a reference. We also identify gaps that require further research and discuss future directions in assessing agreement.

show abstract

Structural Equation Model

Carrasco¹

2012

Encyclopedia of Biopharmaceutical Statistics, Third Edition

View full text Add to dashboard Cite

The Structural Error-in-Equation Model to Evaluate Individual Bioequivalence

Cited by 4 publications

References 22 publications

A Review on Assessing Agreement

A Review on Assessing Agreement

An Overview on Assessing Agreement with Continuous Measurements

Structural Equation Model

Contact Info

Product

Resources

About