Effectively extracting biological insights from multi-omics datasets remains challenging. Many multi-omics analyses require matched samples (each type of omics data measured on each sample), which can greatly reduce the analysis sample size. Further, many dimension-reduction based analyses are applied to whole-genome data, which can be computationally demanding. Here we present the R package pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcomes. Our package is computationally efficient and does not require matched samples in multi-omics data. The input of pathwayMultiomics is p-values at the level of biological pathways for individual omics data types, which are then integrated using the novel MiniMax statistic to prioritize pathways dysregulated across multiple omics datasets. We show in silico that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real Alzheimer’s disease datasets to show that pathwayMultiomics was able to nominate biologically meaningful pathways.