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With the increasing availability of real-world data including electronic health records (EHR) data and claims data, it is important to effectively integrate evidence from multiple data sources to enable reproducible scientific discovery. However, we are still facing practical challenges in data integration, such as protection of data privacy, the high dimensionality of features, and heterogeneity across different datasets. Aim to facilitate efficient multi-institutional data analysis without sharing individual patient data (IPD), we developed a toolbox of Privacy-preserving Distributed Algorithms (PDA) that conduct distributed learning and inference for various models, such as logistic regression, Cox model, Poisson model, high-dimensional penalized regression, mixed effects models, mixture models, and beyond. Our algorithms do not require iterative communication across sites and are able to account for heterogeneity across different hospitals. In addition, PDA outperforms meta-analysis methods in many settings including pharmacovigilance applications, with a focus on clinical evidence generation. The validity and efficiency of PDA are also demonstrated with real-world use cases in Observational Health Data Sciences and Informatics (OHDSI), PCORnets including PEDSnet and OneFlorida, and Penn Medicine Biobank (PMBB).