Description Usage Arguments Details Value References Examples
This function performs metaanalytic studies of diagnostic tests for both the fixed and randomeffects models. In particular it reports a fully nonparametric ROC curve estimate when data come from a metaanalysis study using the information of all cutoff points available in the selected original studies. The approach considered is the one proposed by MartinezCamblor et al. (2017) based on weighting each individual interpolated ROC curve. See References below.
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data 
a data frame containing at least the following variables (with these names):

Ni 
number of points of the unit interval (FPR values) considered to calculate the curve. Default: 1000. 
model 
the metaanalysis model used to estimate the ROC curve. One of "fixedeffects" (it only considers the withinstudy variability) or "randomeffects" (it takes into account the variability between the studies). 
plot.Author 
if TRUE, a plot including ROC curve estimates (by linear interpolation) for each paper under study is displayed. 
plot.bands 
if TRUE, confidence interval estimate for the curve is added to the plot of the ROC curve estimate. 
plot.inter.var 
if TRUE, a plot including interstudy variability estimate is displayed on an additional window. 
cex.Author 
the magnification to be used to display the paper/author points labels relative to the current setting of 
lwd.Author 
the size to be used for the paper/author points. 
col.curve 
the color to be used for the (summary) ROC curve estimate. Default: blue. 
col.bands 
the color to be used for the confidence interval of ROC curve estimate. Default: light blue. 
alpha.trans 
proportion of opacity to be used for the confidence interval of ROC curve estimate. A number in the unit interval where 0 means transparent. Default: 0.5. 
col.border 
the color to be used for the border of confidence interval of ROC curve estimate. Default: blue. 
... 
another graphical parameters to be passed. 
The slight modification considered to ensure the monotonicity of the summary ROC curve estimate is the following sRA(t) = max(sup_{z \in [0,t]} sRA(z), RA(t)).
Some basic information about the model used and the results obtained are printed.
data 
the dataframe considered ordered by AuthorFPRTPR and including the following variables:

t 
values of the unit interval (FPR values) considered to calculate the curve. 
model 
the metaanalysis model used to estimate the ROC curve. One of "fixedeffects" (it only considers the withinstudy variability) or "randomeffects" (it takes into account the variability between the studies). 
sRA 
nonparametric summary ROC curve estimate following the 
RA 
nonparametric summary ROC curve estimate following the 
se.RA 
standarderror of summary ROC curve estimate. 
area 
area under the summary ROC curve estimate by trapezoidal rule. 
youden.index 
the optimal specificity and sensitivity (in the Youden index sense). 
roc.j 
a matrix whose column j contains the estimated ROC curve for the jth study in each point 
w.j 
a matrix whose column j contains the weights in fixedeffects model for the jth study in each point 
w.j.rem 
a matrix whose column j contains the weights in randomeffects model for the jth study in each point 
inter.var 
interstudy variability estimate in each point 
MartinezCamblor P., 2017, Fully nonparametric receiver operating characteristic curve estimation for randomeffects metaanalysis, Statistical Methods in Medical Research, 26(1), 520.
1 2 3 4 5 6 7 8  data(interleukin6)
# Fixedeffects metaanalysis showing linear interpolations of the papers considered in the graphic
output1 < metaROC(interleukin6, plot.Author=TRUE)
# Randomeffects metaanalysis displaying also a window with a plot of the interstudy
# variability estimate
output2 < metaROC(interleukin6, model="randomeffects", plot.Author=TRUE)

Number of papers included in metaanalysis: 9
Model considered: fixedeffects
The area under the summary ROC curve (AUC) is 0.7721521.
The optimal specificity and sensibility (in the Youden index sense) for summary ROC curve are 0.6996997 and 0.7597871, respectively.
Number of papers included in metaanalysis: 9
Model considered: randomeffects
The area under the summary ROC curve (AUC) is 0.7881458.
The optimal specificity and sensibility (in the Youden index sense) for summary ROC curve are 0.7007007 and 0.7625716, respectively.
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