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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/SummaryPoints.R

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

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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 <[email protected]>

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

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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)

Example output

Loading required package: mvtnorm
Loading required package: ellipse
Loading required package: mvmeta
This is mvmeta 0.4.7. For an overview type: help('mvmeta-package').
Call:  reitsma.default(data = Dementia)

Fixed-effects coefficients:
              tsens     tfpr
(Intercept)  1.3173  -2.0523

33 studies, 2 fixed and 3 random-effects parameters
   logLik        AIC        BIC  
  61.2027  -112.4053  -101.4570  

$posLR
[1] 7.050661

$negLR
[1] 0.2396612

$invnegLR
[1] 4.221169

$DOR
[1] 29.718

          Mean Median  2.5%  97.5%
posLR     7.05  7.000  5.34  9.070
negLR     0.24  0.239  0.19  0.291
invnegLR  4.22  4.180  3.44  5.250
DOR      29.70 29.200 21.10 40.800
             Mean   Median      10%      90%
posLR     7.05070  7.00280  5.84490  8.27720
negLR     0.23966  0.23933  0.20681  0.27225
invnegLR  4.22120  4.17840  3.67320  4.83540
DOR      29.71800 29.24200 23.81100 36.34900
      Mean Median   2.5% 97.5%
theta 0.11  0.109 0.0877 0.136
Call:
phm.default(data = Dementia)

           Estimate      2.5 %      97.5 %
theta   0.112946737 0.08762856 0.138264912
taus_sq 0.004070198 0.00052985 0.007610547

Log-likelihood: 69.998 on 2 degrees of freedom
AIC:  -136 
BIC:  -133 

	Chi-square goodness of fit test (Adjusted Profile Maximum Likelihood
	under heterogeneity)

data:  x
Chi-square = 34.415, df = 2, p-value = 0.3076


   AUC  2.5 % 97.5 %   pAUC  2.5 % 97.5 % 
 0.899  0.919  0.879  0.835  0.868  0.803 
        Mean Median  2.5% 97.5%
Youden 0.672  0.673 0.626 0.714

mada documentation built on May 31, 2017, 3:05 a.m.