Researchers have prepared macros or modules for statistical models for meta-analysis of data from diagnostic test accuracy studies for several statistical analysis software programs. As these become available we will add them to this page. Currently, there is a macro avaibale for SAS and a package for STATA.
MetaDAS: A SAS macro for meta-analysis of diagnostic accuracy studies, contains both the bivariate and the HSROC model. Please find the required documents hereunder:
- User guide version 1.3 (2012). (PDF 2.7MB, opens in new window)
- Quick reference and worked example (2012). (PDF 2.6MB, opens in new window)
- The SAS macro itself: METADAS v1.3. This is provided as a text-file and opens in a new window.
There are several user-written packages for conducting meta-analysis of diagnostic test accuracy (DTA) studies in R. This tutorial summarises and illustrates some of the packages. Step-by-step instructions are also provided for carrying out the bivariate binomial method by fitting a generalized linear mixed model (GLMM) using the glmer function in the R package lme4. A .R file, “Bivariate binomial meta-analysis of diagnostic test accuracy studies.R” and example dataset based on a review by Schuetz et al. 2010, are included with the tutorial in the zipped folder.
METANDI: A Stata user-written package for meta-analysis of diagnostic accuracy studies (Harbord and Whiting 2009; Harbord 2008). metandi performs bivariate meta-analysis of sensitivity and specificity using a generalized linear mixed model approach (Reitsma et al 2005; Chu & Cole 2006). In Stata 10 and above, metandi fits the model using the built in command xtmelogit by default. In Stata 8 or 9, metandi uses the user written function gllamm (Rabe-Hesketh et al 2004). metandi can be found from within Stata by typing 'findit metandi' or installed from within Stata by typing 'ssc install metandi' (while connected to the internet). You may also need to install gllamm.
METANDI cannot be used to formally investigate heterogeneity or to compare the accuracy of two or more tests because it does not have an option for including a covariate in the bivariate model. However, such analyses are possible by using xtmelogit directly. Here is a practical tutorial to guide both novice and experienced Stata users on how to fit the bivariate model using metandi or xtmelogit. The example dataset used in the tutorial, schuetz.csv, is based on a published diagnostic test accuracy review (Schuetz et al. 2010). A do-file, "Meta-analysis of test accuracy studies in Stata.do", accompanies this tutorial.
Chu H, Cole SR. Bivariate meta-analysis for sensitivity and specificity with sparse data: a generalized linear mixed model approach (letter to the Editor). J Clin Epidemiol. 2006;59(12):1331-2.
Harbord RM, Whiting P. metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression. Stata Journal. 2009;9(2):211-29.
Harbord, R. metandi: Stata module for meta-analysis of diagnostic accuracy. Statistical Software Components, Boston College Department of Economics. Revised 15 Apr 2008.
Rabe-Hesketh S, Skrondal A, Pickles A. "GLLAMM Manual" (October 2004). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 160. Accessed at http://biostats.bepress.com/ucbbiostat/paper160 on 31 October 2013.
Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982-90.
Schuetz GM, Zacharopoulou NM, Schlattmann P, Dewey M. 2010. Meta-analysis: noninvasive coronary angiography using computed tomography versus magnetic resonance imaging. Ann Intern Med. 2010;152(3):167-77.