A consensus score to combine inferences from multiple centres

Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators are challenging in design, data collection and inferences. This may lead to inconsistent results across the resources. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology.