[Multiple comparisons in categorical data analysis].
OBJECTIVE
To discuss the method for multiple comparisons of categorical data and propose an approach to deal with the percentage data.


METHODS
The method of multiple comparisons for percentages was verified based on Bonferroni methodology and Monte Carlo method using SAS 9.13 software.


RESULTS
The type I error could be enlarged if the statistical tests were conducted without adjustment of the significant level after dividing the data of several categories or percentages into several four-fold tables. For the percentage data, the correction of adjustment of the significant level was the number of pairwise comparison minus one, as supported by the results of Monte Carlo simulation.


CONCLUSIONS
Multiple comparisons of categorical data should be applied appropriately. Multiple comparisons of percentages data need to be conducted with the number of pairwise comparison minus one.
Cao
Rui
R
Department of Biostatistics, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Qian
Jun
J
Chen
Ping-yan
PY
D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012984:Software; D013223:Statistics as Topic / Q000379:methods*