SummaryPts {mada} | R Documentation |
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 |
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 |
alphasens |
numeric, alpha parameter for the sensitivities. Amounts to logit transformation if set to 1 (the default). See |
alphafpr |
numeric, alpha parameter for the false positive rates. Amounts to logit transformation if set to 1 (the default). See |
n.iter |
number of samples |
FUN |
A list of functions with 2 arguments ( |
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 <philipp.doebler@googlemail.com>
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
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)