Open AccessMetaplot: A new Stata module for assessing heterogeneity in a meta-analysis
Author(s) -
Jalal Poorolajal,
Shahla Noornejad
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0253341
Subject(s) - study heterogeneity , computer science , statistics , identification (biology) , table (database) , plot (graphics) , data mining , r package , software , binomial distribution , mathematics , confidence interval , programming language , biology , botany
Background The proposed sequential and combinatorial algorithm, suggested as a standard tool for assessing, exploring, and reporting heterogeneity in the meta-analysis, is useful but time-consuming particularly when the number of included studies is large. Metaplot is a novel graphical approach that facilitates performing sensitivity analysis to distinguish the source of substantial heterogeneity across studies with ease and speed. Method Metaplot is a Stata module based on Stata’s commands, known informally as "ado". Metaplot presents a two-way (x, y) plot in which the x-axis represents the study codes and the y-axis represents the values of I 2 statistics excluding one study at a time (n-1 studies). Metaplot also produces a table in the ’Results window’ of the Stata software including details such as I 2 and χ 2 statistics and their P -values omitting one study in each turn. Results Metaplot allows rapid identification of studies that have a disproportionate impact on heterogeneity across studies, and communicates to what extent omission of that study may reduce the overall heterogeneity based on the I 2 and χ 2 statistics. Metaplot has no limitations regarding the number of studies or types of outcome data (binomial or continuous data). Conclusions Metaplot is a simple graphical approach that gives a quick and easy identification of the studies having substantial influences on overall heterogeneity at a glance.