High hydrostatic pressure processing (HPP) is a mild preservation technique, and its use for processing foods has been widely documented in the literature. However, very few quantitative synthesis studies have been conducted to gather and analyze bacterial inactivation data to identify the mechanisms of HPP-induced bacterial inactivation. The purpose of this study was to conduct a quantitative analysis of three-decimal reduction times (t3δ) from a large set of existing studies to determine the main influencing factors of HPP-induced inactivation of three foodborne pathogens (Listeria monocytogenes, Staphylococcus aureus, and Salmonella enterica) in various foods. Inactivation kinetics data sets from 1995 to 2017 were selected, and t3δ values were first estimated by using the nonlinear Weibull model. Bayesian inference was then used within a metaregression analysis to build and test several models and submodels. The best model (lowest error and most parsimonious) was a hierarchical mixed-effects model including pressure intensity, temperature, study, pH, species, and strain as explicative variables and significant factors. Values for t3δ and ZP associated with inactivation under HPP were estimated for each bacterial pathogen, with their associated variability. Interstudy variability explained most of the variability in t3δ values. Strain variability was also important and exceeded interstudy variability for S. aureus, which prevented the development of an overall model for this pathogen. Meta-analysis is not often used in food microbiology but was a valuable quantitative tool for modeling inactivation of L. monocytogenes and Salmonella in response to HPP treatment. Results of this study could be useful for refining quantitative assessment of the effects of HPP on vegetative foodborne pathogens or for more precisely designing costly and labor-intensive experiments with foodborne pathogens.