  We assessed several agreement coefficients applied in 2x2 contingency tables,
which are commonly applied in research due to dicotomization by the conditions
of the subjects (e.g., male or female) or by conveniency of the classification
(e.g., traditional thresholds leading to separations in healthy or diseased,
exposed or non-exposed, etc.). More extreme table configurations (e.g., high
agreement between raters) are also usual, but some of the coefficients have
problems with imbalanced tables. Here, we not only studied some especific
estimators, but also developed a general method to the study for any estimator
candidate to be an agreement measurement. This method was developed in open
source R codes and it is avaliable to the researchers. Here, we tested this
method by verifying the performance of several traditional estimators over all
1,028,789 tables with size ranging from 1 to 68. Cohen's kappa showed
handicapped behavior similar to Pearson's r, Yule's Q, and Yule's Y. Scott's pi
has ambiguity to assess situations of agreement between raters. Shankar and
Bangdiwala's B was mistaken in all situations of neutrality and when there is
greater disagreement between raters. Dice's F1 and McNemar's chi-squared
incompletely assess the information of the contingency table, showing the
poorest performance among all. We concluded that Holley and Guilford's G is the
best agreement estimator, closely followed by Gwet's AC1 and they should be
considered as the first choices for agreement measurement in contingency 2x2
tables. All procedures and data were implemented in R and are available to
download from https://sourceforge.net/projects/tables2x2.
