This paper presents: (a) the Data-Theoretic methodology as part of an ongoing research which integrates Physics-of-Failure (PoF) theories and data analytics to be applied in Probabilistic Risk Assessment (PRA) of complex systems and (b) the status of the application of the proposed methodology for the estimation of the frequency of the location-specific loss-of-coolant accident (LOCA), which is a critical initiating event in PRA and one of the challenges of the risk-informed resolution for Generic Safety Issue 191 (GSI-191) . The proposed methodology has the following unique characteristics: (1) it uses predictive causal modeling along with sensitivity and uncertainty analysis to find the most important contributing factors in the PoF models of failure mechanisms. This model-based approach utilizes importance-ranking techniques, scientifically reduces the number of factors, and focuses on a detailed quantification strategy for critical factors rather than conducting expensive experiments and time-consuming simulations for a large number of factors. Th is adds validity and practicality to the proposed methodology. (2) Because of the evolving nature of computational power and information-sharing technologies, the Data-Theoretic method for PRA expands the classical approach of data extraction and implementation for risk analysis. It utilizes advanced data analytic techniques (e.g., data mining and text mining) to extract risk and reliability information from diverse data sources (academic literature, service data, regulatory and laboratory reports, expert opinion, maintenance logs, news, etc.) and executes them in theory-based PoF networks. (3) The Data-Theoretic approach uses comprehensive underlying PoF theory to avoid potentially misleading results from use of solely data-oriented approaches, as well as support the completeness of the contextual physical factors and the accuracy of their causal relationships. (4) When the important factors are identified, the Data-Theoretic approach applies all potential theory-based techniques (e.g., simulation and experimentation).