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MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies

Author

Listed:
  • Ben Dwamena

    (Division of Nuclear Medicine, Department of Radiology, University of Michigan Health System, Ann Arbor)

Programming Language

Abstract

midas is a user-written command for idiot-proof implementation of some of the contemporary statistical methods for meta-analysis of binary diagnostic test accuracy. Primary data synthesis is performed within the bivariate mixed-effects logistic regression modeling framework. Likelihood-based estimation is by adaptive gaussian quadrature using xtmelogit (Stata release 10) with post-estimation procedures for model diagnostics and empirical Bayes predictions. Average sensitivity and specificity (optionally depicted in SROC space with or without confidence and prediction regions), and their derivative likelihood and odds ratios are calculated from the maximum likelihood estimates. midas facilitates exploratory analysis of heterogeneity, threshold-related variability, methodological quality bias, publication and other precision-related biases. Bayes' nomograms, likelihood-ratio matrices, and probability modifying plots may be derived and used to guide patient-based diagnostic decision making. A dataset of studies evaluating axillary staging performance of positron emission tomography in breast cancer patients is provided for illustration of the omnibus capabilities of midas.

Suggested Citation

  • Ben Dwamena, 2007. "MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies," Statistical Software Components S456880, Boston College Department of Economics, revised 05 Feb 2009.
    • Handle: RePEc:boc:bocode:s456880
    Note: This module should be installed from within Stata by typing "ssc install midas". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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    File URL: http://fmwww.bc.edu/repec/bocode/m/midas.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/f/fagani.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/h/homogeni.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/l/lrmat.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/p/pddami.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/m/midas.sthlp
    File Function: help file
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    File URL: http://fmwww.bc.edu/repec/bocode/m/midas.pdf
    File Function: documentation
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    File URL: http://fmwww.bc.edu/repec/bocode/m/midas_example.dta
    File Function: sample data file
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    Citations

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    Cited by:

    1. Bingsheng Li & Aihua Gan & Xiaolong Chen & Xinying Wang & Weifeng He & Xiaohui Zhang & Renxiang Huang & Shuzhu Zhou & Xiaoxiao Song & Angao Xu, 2016. "Diagnostic Performance of DNA Hypermethylation Markers in Peripheral Blood for the Detection of Colorectal Cancer: A Meta-Analysis and Systematic Review," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-13, May.
    2. Jiayuan Wu & Liren Hu & Gaohua Zhang & Fenping Wu & Taiping He, 2015. "Accuracy of Presepsin in Sepsis Diagnosis: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    3. Matthew Quaife & Fern Terris-Prestholt & Gian Luca Di Tanna & Peter Vickerman, 2018. "How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1053-1066, November.
    4. Guocan Yu & Wuchen Zhao & Yanqin Shen & Pengfei Zhu & Hong Zheng, 2020. "Metagenomic next generation sequencing for the diagnosis of tuberculosis meningitis: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-12, December.

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