Evaluating Bias and Precision in Method Comparison Studies

Evaluate bias and precision in method comparison studies. One provides measurements for each method and it takes care of the estimates. Multiple plots to evaluate bias, precision and compare methods.

MethodCompare

The goal of MethodCompare is to implement the methodology reported in the two papers : “Effective plots to assess bias and precision in method comparison studies” (P. Taffé) published in Statistical Methods in Medical Research (2018; 27:1650-1660), “Assessing bias, precision, and agreement in method comparison studies” (P. Taffé) published in Statistical Methods in Medical Research (2020; 29:778-796). The package will generate graphs (the bias, total bias, precision, comparison, agreement without and after recalibration, percentage of agreement without and after recalibration, mean squared error and square root mean squared error plots), compute the differential and proportional biases with their respective 95% CIs.

Installation

You can install the development version of MethodCompare from GitHub with:

# install.packages("devtools")
devtools::install_github("UBERLULU/MethodCompare")

Example

This is a basic example which shows you how to solve a common problem:

library(MethodCompare)
## basic example code
### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1)
#> [1] "Computing differential and proportional biases"
#> [1] "id variable: id"
#> [1] "New method y variable: y1"
#> [1] "Reference method y variable: y2"
#> [1] "Number of simulations set to 1000"

### Display bias plot
bias_plot(measure_model)
#> [1] "Generating Bias Plot ..."