reitsma | R Documentation |
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).
reitsma(data, ...) ## Default S3 method: reitsma(data = NULL, subset=NULL, formula = NULL, TP="TP", FN="FN", FP="FP", TN="TN", alphasens = 1, alphafpr = 1, correction = 0.5, correction.control = "all", method = "reml", control = list(), ...)
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
, while for positive or negative predictive values, see predv_r
and predv_d
.
An object of the class reitsma
for which many standard methods are available. See reitsma-class
for details.
Philipp Doebler <philipp.doebler@googlemail.com>
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
data(Dementia) (fit <- reitsma(Dementia)) summary(fit) plot(fit) ## Meta-Regression data(smoking) # contains more than one 2x2-table ## reduce to subset of independent 2x2-tables by using the ## first table from each study only smoking1 <- subset(smoking, smoking$result_id == 1) ## use type of questionnaire as covariate (fit <- reitsma(smoking1, formula = cbind(tsens, tfpr) ~ type)) summary(fit) ## sensitivities significantly lower for SAQ
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.