This R-package enables meta-analysis of full diagnostic test accuracy studies and ROC-curves using various techniques.
- For the R-code used in the analyses of A discrete time-to-event model for the meta-analysis of full ROC curves, see the branch discrete_GLMM_paper
- For the R-code used in the analyses of Increasing flexibility for the meta-analysis of full ROC curves – a copula approach, see the branch copulas
- For the R-code used in the analyses of Comparison of different methods for the meta-analysis of diagnostic test accuracy studies – a simulation study, see the branch simstudy
Features
-
simulate data from the following models:
- discrete GLMMs with categorical variable threshold using either the cloglog- or the logit-link as proposed in Stoye et al. (2024)
- logit LMM as proposed in Steinhauser et al. (2016)
- Weibull AFT model with bivariate random effect as proposed in Hoyer et al. (2018)
- survival copula models with different marginal distributions. Currently available copulas: Clayton copula, asymmetric Joe copula. Currently available marginals: Weibull-binomial, Weibull-normal, loglogistic-binomial, loglogistic-normal, lognormal-binomial, lognormal-normal
-
estimate the following models to data from several DTA studies reporting results for multiple diagnostic thresholds per study:
- discrete GLMMs with categorical variable threshold using either the cloglog- or the logit-link (Stoye et al., 2024)
- logit LMM (Steinhauser et al., 2016) using a link to the package
diagmeta - survival copula models with different marginal distributions. Currently available copulas: Clayton copula, asymmetric Joe copula. Currently available marginals: Weibull-binomial, Weibull-normal, loglogistic-binomial, loglogistic-normal, lognormal-binomial, lognormal-normal
- non-parametric SROC model (Martínez-Camblor, 2017) using a link to the package
nsROC - logit GLMM (Hoyer & Kuss, 2018)
- semi-parametric global rank-based model (Frömke et al., 2022) using a link to the package
diagacc
-
estimate the following models to data from several DTA studies reporting results for a single diagnostic threshold per study:
- SROC model (Moses et al., 1993)
- bivariate LMM (Reitsma et al., 2005) using a link to the package
mada - bivariate GLMM (Chu & Cole, 2010)
- SROC Lehmann model (Holling et al., 2012) using a link to the package
mada - beta copula model (Nikoloulopoulos, 2015) using a link to the package
CopulaREMADA
evaluate estimated models based on information on the true data-generating process (for each model individually or aggregated for a number of simulation iterations)
Installation
You can install this package branch (e.g.) using the following code in your R console:
pak::pak("git::https://gitlab.ub.uni-bielefeld.de/stoyef/metaROC")
Usage
All main functionalities are implemented in the function metaROC. See the examples in the function documentation or the vignette Introduction for an introduction.