Accuracy evaluation of the unified P-value from combining correlated P-values

PLoS One. 2014 Mar 24;9(3):e91225. doi: 10.1371/journal.pone.0091225. eCollection 2014.


Meta-analysis methods that combine P-values into a single unified P-value are frequently employed to improve confidence in hypothesis testing. An assumption made by most meta-analysis methods is that the P-values to be combined are independent, which may not always be true. To investigate the accuracy of the unified P-value from combining correlated P-values, we have evaluated a family of statistical methods that combine: independent, weighted independent, correlated, and weighted correlated P-values. Statistical accuracy evaluation by combining simulated correlated P-values showed that correlation among P-values can have a significant effect on the accuracy of the combined P-value obtained. Among the statistical methods evaluated those that weight P-values compute more accurate combined P-values than those that do not. Also, statistical methods that utilize the correlation information have the best performance, producing significantly more accurate combined P-values. In our study we have demonstrated that statistical methods that combine P-values based on the assumption of independence can produce inaccurate P-values when combining correlated P-values, even when the P-values are only weakly correlated. Therefore, to prevent from drawing false conclusions during hypothesis testing, our study advises caution be used when interpreting the P-value obtained from combining P-values of unknown correlation. However, when the correlation information is available, the weighting-capable statistical method, first introduced by Brown and recently modified by Hou, seems to perform the best amongst the methods investigated.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Data Interpretation, Statistical
  • Meta-Analysis as Topic*
  • Statistics as Topic / methods*