SummaryPts: Use the Zwindermann & Bossuyt (2008) MCMC procedure to...

In mada: Meta-Analysis of Diagnostic Accuracy

Use the Zwindermann & Bossuyt (2008) MCMC procedure to generate summary points (positive and negative likelihood ratio, diagnostic odds ratio) for the Reitsma et al. (2005) bivariate model

Description

Zwindermann & Bossuyt (2008) argue that likelihood ratios should not be pooled by univariate meta-analysis. They propose a sampling based approach that uses the parameters of a fit to the bivariate model (implemented in reitsma) to generate samples for observed sensitivities and false positive rates. From these samples the quantities of interest (and their confidence intervals) are estimated.

Usage

SummaryPts(object, ...)
## Default S3 method:
SummaryPts(object, mu,Sigma,alphasens = 1, alphafpr = 1,
                           n.iter = 10^6, FUN, ...)
## S3 method for class 'reitsma'
SummaryPts(object, n.iter = 10^6, FUN = NULL, ...)
## S3 method for class 'SummaryPts'
print(x, ...)
## S3 method for class 'SummaryPts'
summary(object, level = 0.95, digits = 3, ...)

Arguments

object	an object for which a method exists
x	An object of class SummaryPts
mu	numeric of length 2, expected to be the mean parameter of a bivariate model
Sigma	2x2 variance covariance matrix, expected to be the matrix representing the standard error of mu and the covariance of these two estimates
alphasens	numeric, alpha parameter for the sensitivities. Amounts to logit transformation if set to 1 (the default). See reitsma.
alphafpr	numeric, alpha parameter for the false positive rates. Amounts to logit transformation if set to 1 (the default). See reitsma.
n.iter	number of samples
FUN	A list of functions with 2 arguments (sens and fpr); if set to NULL in SummaryPts.reitsma, the positive, negative and inverse negative likelihood ratios are calculated and also the diagnostic odds ratio (DOR). See the example on how to supply other functions.
level	numeric, confidence level for confidence intervals
digits	number of significant digits to display
...	arguments to be passed on to other functions, currently ignored

Details

Samples are generated from a bivariate normal using rmvnorm. Note that the FUN argument

Value

An object of the class SummaryPts for which print and summary methods are available.

Author(s)

Philipp Doebler <[e-mail]>

References

Reitsma, J., Glas, A., Rutjes, A., Scholten, R., Bossuyt, P., & Zwinderman, A. (2005). “Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews.” Journal of Clinical Epidemiology, 58, 982–990.

Zwinderman, A., & Bossuyt, P. (2008). “We should not pool diagnostic likelihood ratios in systematic reviews.”Statistics in Medicine, 27, 687–697.

See Also

reitsma, talpha

Examples

data(Dementia)
(fit <- reitsma(Dementia))
mcmc_sum <- SummaryPts(fit, n.iter = 10^3)
## n.iter should be larger in applications!
mcmc_sum #just the means
summary(mcmc_sum) # 95% CIs by default
summary(mcmc_sum, level = 0.80, digits = 5) ## more digits, smaller CIs

## Supplying other functions

# Example 1: theta parameter of proportional hazards model 
# see "phm" in mada's documentation for details on theta 
theta <- function(sens,fpr){log(sens) / log(fpr)}
theta_sum <- SummaryPts(fit, FUN = list(theta = theta), n.iter = 10^3)
## n.iter should be larger in applications!
summary(theta_sum)
# compare with phm:
summary(phm(Dementia)) # the two estimators almost agree in this example

# Example 2: Youden index
Youden <- function(sens, fpr){sens - fpr}
Youden_sum <- SummaryPts(fit, FUN = list(Youden = Youden), , n.iter = 10^3)
## n.iter should be larger in applications!
summary(Youden_sum)

mada documentation built on Sept. 9, 2022, 3:01 p.m.