Accuracy Evaluation of the Unified P-Value from Combining Correlated P-Values

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