metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression

Roger Harbord and Penny Whiting
Additional contact information
Penny Whiting: University of Bristol

Stata Journal, 2009, vol. 9, issue 2, 211-229

Abstract: Meta-analysis of diagnostic test accuracy presents many challenges. Even in the simplest case, when the data are summarized by a 2 × 2 table from each study, a statistically rigorous analysis requires hierarchical (multilevel) models that respect the binomial data structure, such as hierarchical logistic regression. We present a Stata package, metandi, to facilitate the fitting of such models in Stata. The commands display the results in two alternative parameterizations and produce a customizable plot. metandi requires either Stata 10 or above (which has the new command xtmelogit), or Stata 8.2 or above with gllamm installed. Copyright 2009 by StataCorp LP.

Keywords: metandi; metandiplot; diagnosis; meta-analysis; sensitivity and specificity; hierarchical models; generalized mixed models; gllamm; xtmelogit; re- ceiver operating characteristic (ROC); summary ROC; hierarchical summary ROC (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0163 link to article download
http://www.stata-journal.com/software/sj9-2/st0163/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:9:y:2009:i:2:p:211-229

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().