Package 'MethodCompare'

Title: Evaluating Bias and Precision in Method Comparison Studies
Description: 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.
Authors: Thomas Blomet [aut, cre], Mingkai Peng [aut], Patrick Taffé [aut], Tyler Williamson [aut]
Maintainer: Thomas Blomet <[email protected]>
License: GPL (>= 3)
Version: 1.1.0
Built: 2025-02-27 05:17:09 UTC
Source: https://github.com/uberlulu/methodcompare

Help Index

Plot the agreement before recalibration

Description

This function draws the "agreement plot" before recalibration, which is used to visually appraise the degree of agreement between the new and reference methods, before recalibration of the new method. It is obtained by graphing a scatter plot of y1-y2 (difference of the methods) versus the BLUP of the latent trait, x, along with the bias and 95% limits of agreement with their 95% simultaneous confidence bands. The function adds a second scale on the right axis, showing the percentage of agreement index.

Usage

agreement0(object, rarea = FALSE)

Arguments

object

list returned by measure_compare function.
rarea

if TRUE, draw the plot with shading areas between the confidence bands.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the agreement without recalibration
agreement0(measure_model)

Plot the agreement after recalibration

Description

This function draws the "agreement plot" after recalibration, which is used to visually appraise the degree of agreement between the new and reference methods, before recalibration of the new method. It is obtained by graphing a scatter plot of y1-y2 (difference of the methods) versus the BLUP of the latent trait, x, along with the bias and 95% limits of agreement with their 95% simultaneous confidence bands. The function adds a second scale on the right axis, showing the percentage of agreement index.

Usage

agreement1(object, rarea = FALSE)

Arguments

object

list returned by measure_compare function.
rarea

if TRUE, draw the plot with shading areas between the confidence bands.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the agreement after recalibration
agreement0(measure_model)

Plot the bias and measurements

Description

This function draws the "bias plot", which is used to visually assess the bias of the new method relative to the reference method. It is obtained by graphing a scatter plot of y1 (new method) and y2 (reference method) versus the BLUP of the latent trait, x, along with the two regression lines. The function adds a second scale on the right axis, showing the relationship between the estimated amount of bias and BLUP of the latent trait, x.

Usage

bias_plot(object)

Arguments

object

list returned by measure_compare function.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the bias
bias_plot(measure_model)

Plot used to visualize the recalibration of the new method after estimating the bias

Description

This function allows the visualization of the bias-corrected values (i.e. recalibrated values, variable y1_corr) of the new measurement method.

Usage

compare_plot(object)

Arguments

object

list returned by measure_compare function.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the bias
compare_plot(measure_model)

Simulated dataset 1

Description

In the simulated dataset 1, each subject has 1 to 3 measurement values from the new method and 10 to 20 measurement values from the reference method. Compared to the reference method, the new method has differential bias of 4 and proportional bias of 0.8. Variance of the new method is smaller than that for the reference method.

Usage

data1

Format

data1

An object of class data.frame with 1468 rows and 3 columns

Details

A data frame with 3 variables:

id

identification number for subjects

y1

values from the new measuremment method

y2

values from the reference method

Dataset 1 was created based on the following equations:

y1i=4+0.8xi+ε1i,ε1i∣xi∼N(0,(0.2xi)2)y_{1i}=4+0.8x_i+\varepsilon_{1i},\quad \varepsilon_{1i} \mid x_i \sim N(0,(0.2x_i)^2)

y2i=xi+ε2i,ε2i∣xi∼N(0,(1.75+0.08xi)2)y_{2i}=x_i+\varepsilon_{2i},\quad \varepsilon_{2i} \mid x_i \sim N(0,(1.75+0.08x_i)^2)

xi∼Uniform[25−45]x_i\sim Uniform[25-45]

for i=1,…,100i=1,\ldots,100 and the number of repeated measurements for each subject ii from the reference standard was n2i∼Uniform[10,20]n_{2i} \sim Uniform[10,20] and n1i∼Uniform[1,3]n_{1i} \sim Uniform[1,3] for the new measurement method.

Simulated dataset 2

Description

In the simulated dataset 2, each subject has 10 to 20 measurement values from the new method and 10 to 20 measurement values from the reference method. Compared to the reference method, the new method has differential bias of 4 and proportional bias of 0.8. Variance of the new method is smaller than that for the reference method.

Usage

data2

Format

data2

An object of class data.frame with 1680 rows and 3 columns

Details

A data frame with 3 variables:

id

identification number for subjects

y1

values from the new measuremment method

y2

values from the reference method

Dataset 1 was created based on the following equations:

y1i=4+0.8xi+ε1i,ε1i∣xi∼N(0,(0.2xi)2)y_{1i}=4+0.8x_i+\varepsilon_{1i},\quad \varepsilon_{1i} \mid x_i \sim N(0,(0.2x_i)^2)

y2i=xi+ε2i,ε2i∣xi∼N(0,(1.75+0.08xi)2)y_{2i}=x_i+\varepsilon_{2i},\quad \varepsilon_{2i} \mid x_i \sim N(0,(1.75+0.08x_i)^2)

xi∼Uniform[20−100]x_i\sim Uniform[20-100]

for i=1,…,100i=1,\ldots,100 and the number of repeated measurements for each subject ii from the reference standard was n2i∼Uniform[10,20]n_{2i} \sim Uniform[10,20] and n1i∼Uniform[10,20]n_{1i} \sim Uniform[10,20] for the new measurement method.

Simulated dataset 3

Description

In the simulated dataset 3, each subject has 10 to 20 measurement values from the new method and 10 to 20 measurement values from the reference method. Compared to the reference method, the new method has differential bias of 1 and proportional bias of 0.9. Variance of the new method is smaller than that for the reference method.

Usage

data3

Format

data3

An object of class data.frame with 1682 rows and 3 columns

Details

A data frame with 3 variables:

id

identification number for subjects

y1

values from the new measuremment method

y2

values from the reference method

Dataset 1 was created based on the following equations:

y1i=1+0.9xi+ε1i,ε1i∣xi∼N(0,(1+0.04xi)2)y_{1i}=1+0.9x_i+\varepsilon_{1i},\quad \varepsilon_{1i} \mid x_i \sim N(0,(1+0.04x_i)^2)

y2i=xi+ε2i,ε2i∣xi∼N(0,(1.75+0.08xi)2)y_{2i}=x_i+\varepsilon_{2i},\quad \varepsilon_{2i} \mid x_i \sim N(0,(1.75+0.08x_i)^2)

xi∼Uniform[20−100]x_i\sim Uniform[20-100]

for i=1,…,100i=1,\ldots,100 and the number of repeated measurements for each subject ii from the reference standard was n2i∼Uniform[10,20]n_{2i} \sim Uniform[10,20] and n1i∼Uniform[10,20]n_{1i} \sim Uniform[10,20] for the new measurement method.

Estimation of the amount of bias of the new measurement method relative to the reference method

Description

measure_compare() implements the methodology reported in the paper: Taffé P. Effective plots to assess bias and precision in method comparison studies. Stat Methods Med Res 2018;27:1650-1660. Other relevant references: Taffé P, Peng M, Stagg V, Williamson T. Biasplot: A package to effective plots to assess bias and precision in method comparison studies. Stata J 2017;17:208-221. Taffé P, Peng M, Stagg V, Williamson T. MethodCompare: An R package to assess bias and precision in method comparison studies. Stat Methods Med Res 2019;28:2557-2565. Taffé P, Halfon P, Halfon M. A new statistical methodology to assess bias and precision overcomes the defects of the Bland & Altman method. J Clin Epidemiol 2020;124:1-7. Taffé P. Assessing bias, precision, and agreement in method comparison studies. Stat Methods Med Res 2020;29:778-796. Taffé P. When can the Bland-Altman limits of agreement method be used and when it should not be used. J Clin Epidemiol 2021; 137:176-181.

Usage

measure_compare(
  data,
  new = "y1",
  ref = "y2",
  id = "id",
  nb_simul = 1000,
  if_value = NULL
)

Arguments

data

a required data frame containing the identification number of the subject (id), the measurement values from the new method (y1) and those from the reference method (y2).
new

an optional string. The column name containing the measurements of the new measurement method.
ref

an optional string. The column name containing the measurements of the reference method (at least two measurements per subject).
id

an optional string. The column name containing the subject identification numbers.
nb_simul

an optional number. The number of simulations used for simultaneous confidence bands.
if_value

an optional number. Restrict the study to observed measurement greater than the provided value, i.e., y1 >= if_value && y2 >= if_value.

Value

The function returns a list with the following items:

  • models: a list of models fitted in estimation procedure

  • data: the original data frame with renamed columns and additional computed data

  • sim_params: estimated model coefficients used afterward

  • nb_simul: the number of simulations used for confidence bands simulations

  • bias: differential and proportional biases for new method and the associated 95 percent confidence intervals

  • methods: a list of methods names provided by the user

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)

Plot the mean squared errors

Description

This function draws the "MSE plot", which is used to compare the precision of the two measurement methods without recalibrating the new method. It is obtained by graphing the mean squared errors of y1 (new method) and y2 (reference method) versus the BLUP of the latent trait, x, along with their 95% simultaneous confidence bands.

Usage

mse(object, rarea = FALSE)

Arguments

object

list returned by measure_compare function.
rarea

if TRUE, draw the plot with shading areas between the confidence bands.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the mean squared errors
mse(measure_model)

Plot the percentage agreement before recalibration

Description

This function draws the "percentage agreement plot" before recalibration, which shows the amount of percentage agreement. It is obtained by graphing the percentage agreement index before recalibration versus the BLUP of the latent trait, x, along with its 95% simultaneous confidence bands.

Usage

pct_agreement0(object)

Arguments

object

list returned by measure_compare function.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the percentage agreement without recalibration
pct_agreement0(measure_model)

Plot the percentage agreement after recalibration

Description

This function draws the "percentage agreement plot" after recalibration, which shows the amount of percentage agreement. It is obtained by graphing the percentage agreement index after recalibration versus the BLUP of the latent trait, x, along with its 95% simultaneous confidence bands.

Usage

pct_agreement1(object)

Arguments

object

list returned by measure_compare function.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the percentage agreement after recalibration
pct_agreement0(measure_model)

Plot the precision of the methods

Description

This function draws the "precision plot", which allows the visual comparison of the precision (i.e. standard deviation) of the new measurement method with the reference standard by creating a scatter plot of the estimated standard deviations, along with their 95% simultaneous confidence bands, against the best linear prediction (BLUP) of the true latent trait, x.

Usage

precision_plot(object, object2 = NULL, log = FALSE, rarea = FALSE)

Arguments

object

list returned by measure_compare function.
object2

(optional) returned by measure_compare function. If provided, will plot a second precision estimate.
log

if TRUE, guarantee the simultaneous confidence bands around the standard deviation of measurement errors to be strictly positive.
rarea

if TRUE, draw the plot with shading areas between the confidence bands.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the precision of the two methods
precision_plot(measure_model)

Plot the square root of the mean squared errors

Description

This function draws the "sqrt(MSE) plot", which is used to compare the precision of the two measurement methods without recalibrating the new method. It is obtained by graphing the square root mean squared errors of y1 (new method) and y2 (reference method) versus the BLUP of the latent trait, x, along with their 95% simultaneous confidence bands.

Usage

sqrt_mse(object, rarea = FALSE)

Arguments

object

list returned by measure_compare function.
rarea

if TRUE, draw the plot with shading areas between the confidence bands.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the square root mean squared errors
sqrt_mse(measure_model)

Plot total bias

Description

This function draws the "total bias plot", which is used to visually assess the amount of bias. It is obtained by graphing the bias versus the BLUP of the latent trait, x, along with the 95% simultaneous confidence bands.

Usage

total_bias_plot(object, object2 = NULL, rarea = FALSE)

Arguments

object

list returned by measure_compare function.
object2

(optional) returned by measure_compare function. If provided, will plot a second total bias estimate.
rarea

if TRUE, draw the plot with shading areas between the confidence bands.

Examples

### Load the data
data(data1)
### Analysis
measure_model <- measure_compare(data1, nb_simul=100)
### Plot the total bias
total_bias_plot(measure_model)