Package 'VDSPCalibration'

Title: Statistical Methods for Designing and Analyzing a Calibration Study
Description: Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection, regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.
Authors: Ramon Durazo-Arvizu, Chris Sempos, and Lu Tian
Maintainer: Lu Tian <[email protected]>
License: GPL
Version: 1.0
Built: 2024-12-16 06:45:56 UTC
Source: CRAN

Help Index


Statistical Methods for Designing and Analyzing a Calibration Study

Description

Implements statistical methods for designing and analyzing a calibration study

Details

Implements statistical methods for design and analysis of a calibration study. The important functions are "samplesize": for sample size estimation; "sampletot": for sample selection, "calfun": for estimating calibrating equation and "chngpt": for estimating the piece-wise linear equation.

Author(s)

Ramon Durazo-Arvizu, Chris Sempos, and Lu Tian

References

Tian L., Durazo-Arvizu R. A., Myers G., Brooks S., Sarafin K., and Sempos C. T. (2014), The estimation of calibration equations for variables with heteroscedastic measurement errors, Statist. Med., 33, pages 4420-4436


Estimating the Calibration Equation

Description

Estimates the calibration equation based on CV information

Usage

calfun(x, y, CVx, CVy = CVx, lambda0 = 1)

Arguments

x

old VD measurements

y

reference (new) VD measurements

CVx

CV% of the old VD measurements

CVy

CV% of the new VD measurements

lambda0

the CV ratio of the new vs old measurements

Details

Estimation of the calibration equation. It covers 4 scenarios: Only CVx is known; only CVy is known; both CVx and CVy are known; and Only the ratio of CVy to CVx is known.

Value

coef

estimated coefficients of the linear function

se

standard errors of the estimated coefficients

lower CI

the lower end of the 95% CI of the regression coefficients

upper CI

the upper end of the 95% CI of the regression coefficients

Author(s)

Durazo-Arvizu, Ramon; Sempos, Chris; Tian, Lu

References

Tian L., Durazo-Arvizu R. A., Myers G., Brooks S., Sarafin K., and Sempos C. T. (2014), The estimation of calibration equations for variables with heteroscedastic measurement errors, Statist. Med., 33, pages 4420-4436

Examples

n=100
sigma0=10

beta0=5
beta1=1.2
CVx=0.15
CVy=0.07

lambda0=CVy^2/CVx^2

x0=runif(n, 20, 200)
y0=beta0+beta1*x0+rnorm(n)*sigma0
x=x0+x0*CVx*rnorm(n)
y=y0+y0*CVy*rnorm(n)

fit=calfun(x, y, CVx, CVy, lambda0)
fit

Piecewise Regression Estimation

Description

Estimate a piecewise linear regression equation

Usage

chngpt(x, y, start = quantile(x, probs = 0.1,

na.rm = "TRUE"),finish = quantile(x, probs = 0.9, na.rm = "TRUE"),

NbrSteps = 500)

Arguments

x

old VD measurements

y

reference (new) VD measurements

start

lower bound of the changing point

finish

upper bound of the changing point

NbrSteps

number of points used in grid search

Details

This function uses grid search method to fit a piecewise linear regression model with one changing point

Value

x

old VD levels

y

new VD levels

yfitted

calibrated VD levels based on the fitted piecewise linear regression

chngpt

the estimated chang point

coefficients

the estimated regression coefficients for the piecewise linear regression

Author(s)

Durazo-Arvizu, Ramon and Sempos, Chris

References

Tian L., Durazo-Arvizu R. A., Myers G., Brooks S., Sarafin K., and Sempos C. T. (2014), The estimation of calibration equations for variables with heteroscedastic measurement errors, Statist. Med., 33, pages 4420-4436

Examples

### Generate equally spaced TEST VALUES in the interval [20,200]
set.seed(123456789)
x= 20 + 180*1:100/100
x2= (x - 95)*(x>=95)

# Generate REFERENCE VALUES
y = -8 + 1.5*x - 0.85*x2 + 15*rnorm(100)

#Plot test values versus reference values along with fitted piecewise model
plot(x,y)
fit.chngpt = chngpt(x,y)
plot(fit.chngpt$x[order(fit.chngpt$yfitted)],
     fit.chngpt$y[order(fit.chngpt$yfitted)],
     xlim=c(0,200), ylim=c(0,200), xlab="25-Hydroxyvitamin D (nmol/mL), IDS",
     ylab="25-Hydroxyvitamin D (nmol/mL), LC/MS", bty="n", las=1)
lines(fit.chngpt$x[order(fit.chngpt$yfitted)],
      fit.chngpt$yfitted[order(fit.chngpt$yfitted)], lty=2,col=2, lwd=2)
abline(v=fit.chngpt$chngpt, lty=2,col=3, lwd=2)
arrows(fit.chngpt$chngpt+20 ,15, fit.chngpt$chngpt,-8, length=0.1, lwd=2, col=4)
legend(fit.chngpt$chngpt + 5,30, legend=round(fit.chngpt$chngpt, digits=1),
       bty="n", col=4)

Uniformly Sampling

Description

Draws samples uniformly (for internal use only)

Usage

samplefun(x, index, n0)

Arguments

x

The VD values

index

the index for VD value, it can be 1, 2, 3,....

n0

Sample size

Details

Uniform sampling (internal use only)

Value

index

selected ids

x

selected VD levels

Author(s)

Durazo-Arvizu, Ramon, Sempos, Chris and Tian, Lu

See Also

sampletot

Examples

x=rnorm(100)
index=1:100
samplefun(x, index, 40)

Uniform Sampling Within Quartiles

Description

Estimates the sample size to achived the specified precision in the estimated calibration equation.

Usage

samplesize(x0, d0, cutpts = c(7.5, 42.5, 57.5, 72.5, 200), CVx, CVy)

Arguments

x0

The value at which calibration will be esitmated (e.g., 30 nmol/L)

d0

Targeted width of the 95% confidence interval of the calibrated value (e.g. 5nmol/L)

cutpts

Cut points used to define intervals, within which samples would be selected uniformly

CVx

CV% of the old method (e.g. 12%)

CVy

CV% of the reference (new) method (e.g. 5%)

Details

The function estimates the sample size to achived the specified precision in the estimated calibration equation. The precision is defined via x0 and d0

Value

Required sample size to achived the specified precision in the estimated calibration equation.

Author(s)

Durazo-Arvizu, Ramon, Sempos, Chris and Tian, Lu

References

Tian L., Durazo-Arvizu R. A., Myers G., Brooks S., Sarafin K., and Sempos C. T. (2014), The estimation of calibration equations for variables with heteroscedastic measurement errors, Statist. Med., 33, pages 4420-4436

Examples

samplesize(30, 5, cutpts=c(7,42,57,72,200),0.12, 0.05)

Samples Selection

Description

Selects samples used in a calibration study

Usage

sampletot(x, index, n0, K)

Arguments

x

the old sample measurements needing calibration

index

the ID list of the old sample measurements needing calibration

n0

the required sample size

K

the number of quantiles, it is 4 if we use quartiles (recommended)

Details

The function selectes samples used in the calibration study

Value

x

the selected sample measurements to be used in the calibration study

index

the id list of the selected samples to be used in the calibration study

Author(s)

Durazo-Arvizu, Ramon, Sempos, Chris and Tian, Lu

References

Tian L., Durazo-Arvizu R. A., Myers G., Brooks S., Sarafin K., and Sempos C. T. (2014), The estimation of calibration equations for variables with heteroscedastic measurement errors, Statist. Med., 33, pages 4420-4436

Examples

VD.value= 60 + 25*rnorm(1000)
VD.index=1:1000

### x:     the VD value
### index: the index for VD value, it can be 1, 2, 3,....
### n0:    the number of samples we want to select
### K:     the number of quantiles, it is 4 if we use quartiles

sampletot(x=VD.value, index=VD.index, n0=100, K=4)