Description Usage Arguments Details Value Author(s) References See Also Examples
The function fits the bivariate model of Reitsma et al. (2005) that Harbord et al. (2007) have shown to be equivalent to the HSROC of Rutter&Gatsonis (2001). We specify the model as a linear mixed model with known variances of the random effects, similar to the computational approach by Reitsma et al. (2005). Variance components are estimated by restricted maximum likelihood (REML) as a default but ML estimation is available as well. In addition meta-regression is possible and the use of other transformations than the logit, using the approach of Doebler et al. (2012).
1 2 3 4 5 6 7 8 |
data |
any object that can be converted to a data frame with integer variables for observed frequencies of true positives, false negatives, false positives and true negatives. The names of the variables are provided by the arguments |
TP |
character or integer: name for vector of integers that is a variable of |
FN |
character or integer: name for vector of integers that is a variable of |
FP |
character or integer: name for vector of integers that is a variable of |
TN |
character or integer: name for vector of integers that is a variable of |
subset |
the rows of |
formula |
Formula for meta-regression using standard |
alphasens |
Transformation parameter for (continuity corrected) sensitivities, see details. If set to 1 (the default) the logit transformation is used. |
alphafpr |
Transformation parameter for (continuity corrected) false positive rates, see details |
correction |
numeric, continuity correction applied if zero cells |
correction.control |
character, if set to |
method |
character, either |
control |
a list of control parameters, see the documentation of |
.
... |
arguments to be passed on to other functions, currently ignored |
In a first step the observed frequencies are continuity corrected if values of 0 or 1 would result for the sensitivity or false positive rate otherwise. Then the sensitivities and false positive rates are transformed using the transformation
x \mapsto t_α(x) := α\log(x) - (2-α)\log(1-x).
Note that for α=1, the default value, the logit transformation results, i.e. the approach of Reitsma et al. (2005). A bivariate random effects model is then fitted to the pairs of transformed sensitivities and false positive rates.
Parameter estimation makes use of the fact that the fixed effect parameters can be profiled in the likelihood. Internally the function mvmeta
is called. Currently only standard errors for the fixed effects are available. Note that when using method = "mm"
or method = "vc"
, no likelihood can be computed and hence no AIC or BIC values.
If you want other summary points like negative or positive likelihood ratios, see SummaryPts
.
An object of the class reitsma
for which many standard methods are available. See reitsma-class
for details.
Philipp Doebler <[email protected]>
Rutter, C., & Gatsonis, C. (2001). “A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations.” Statistics in Medicine, 20, 2865–2884.
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.
Harbord, R., Deeks, J., Egger, M., Whiting, P., & Sterne, J. (2007). “A unification of models for meta-analysis of diagnostic accuracy studies.” Biostatistics, 8, 239–251.
Doebler, P., Holling, H., Boehning, D. (2012) “A Mixed Model Approach to Meta-Analysis of Diagnostic Studies with Binary Test Outcome.” Psychological Methods, to appear
reitsma-class
, talpha
, SummaryPts
1 2 3 4 5 6 7 8 9 10 |
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
Call: reitsma.default(data = Dementia)
Bivariate diagnostic random-effects meta-analysis
Estimation method: REML
Fixed-effects coefficients
Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
tsens.(Intercept) 1.317 0.150 8.759 0.000 1.023 1.612 ***
tfpr.(Intercept) -2.052 0.171 -11.971 0.000 -2.388 -1.716 ***
sensitivity 0.789 - - - 0.735 0.834
false pos. rate 0.114 - - - 0.084 0.152
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Variance components: between-studies Std. Dev and correlation matrix
Std. Dev tsens tfpr
tsens 0.800 1.000 .
tfpr 0.937 0.578 1.000
logLik AIC BIC
61.203 -112.405 -101.457
AUC: 0.904
Partial AUC (restricted to observed FPRs and normalized): 0.843
HSROC parameters
Theta Lambda beta sigma2theta sigma2alpha
-0.236 3.322 0.158 0.591 0.632
Call: reitsma.default(data = smoking, formula = cbind(tsens, tfpr) ~
type)
Fixed-effects coefficients:
tsens tfpr
(Intercept) 3.0073 -2.4067
typeSAQ -0.9985 0.0186
51 studies, 4 fixed and 3 random-effects parameters
logLik AIC BIC
126.6221 -239.2442 -220.8694
Call: reitsma.default(data = smoking, formula = cbind(tsens, tfpr) ~
type)
Bivariate diagnostic random-effects meta-analysis
Estimation method: REML
Fixed-effects coefficients
Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
tsens.(Intercept) 3.007 0.307 9.809 0.000 2.406 3.608 ***
tsens.typeSAQ -0.999 0.395 -2.531 0.011 -1.772 -0.225 *
tfpr.(Intercept) -2.407 0.243 -9.912 0.000 -2.883 -1.931 ***
tfpr.typeSAQ 0.019 0.307 0.061 0.952 -0.583 0.621
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Variance components: between-studies Std. Dev and correlation matrix
Std. Dev tsens tfpr
tsens 1.286 1.000 .
tfpr 1.003 0.510 1.000
logLik AIC BIC
126.622 -239.244 -220.869
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.