Concordance Rate of a Four-Quadrant Plot for Repeated Measurements

TL;DR

This paper introduces a new concordance rate for four-quadrant plots that accounts for individual data covariance, providing a more accurate and detailed assessment of agreement between clinical measurement methods.

Contribution

The study proposes a novel concordance rate calculation method that considers individual covariance and includes a parameter for minimum agreement, improving evaluation accuracy.

Findings

Proposed method yields more accurate agreement evaluation in simulations.

Real data analysis shows improved interpretability over conventional methods.

Parameter setting enhances detailed understanding of method agreement.

Abstract

Before new clinical measurement methods are implemented in clinical practice, it must be confirmed whether their results are equivalent to those of existing methods. The agreement of the trend between these methods is evaluated using the four-quadrant plot, which describes the trend of change in each difference of the two measurement methods' values in sequential time points, and the plot's concordance rate, which is calculated using the sum of data points in the four-quadrant plot that agree with this trend divided by the number of all accepted data points. However, the conventional concordance rate does not consider the covariance between the data on individual subjects, which may affect its proper evaluation. Therefore, we proposed a new concordance rate calculated by each individual according to the number of agreement. Moreover, this proposed method can set a parameter that the minimum concordant number between two measurement techniques. The parameter can provide a more detailed interpretation of the degree of agreement. A numerical simulation conducted with several factors indicated that the proposed method resulted in a more accurate evaluation. We also showed a real data and compared the proposed method with the conventional approach. Then, we concluded the discussion with the implementation in clinical studies.