In recent years, the increasing availability of in-vehicle data recorder technologies has resulted in more safety studies that have unobtrusively observed the behavior of drivers during actual driving. The research in this paper proposes and applies a flexible analysis structure that can be applied to data that reflect events of interest - including crash data and safety critical event data - from naturalistic driving studies and alert warnings from onboard safety warning systems. The fundamental requirements for the data include a need for GPS and a geographic information system to allow positioning the vehicle on a network and the occurrence and recording of events of interest with attributes, which may include those reflecting the roadway, environment, driver, and event. A distinguishing feature of the formulation is the explicit inclusion of exposure to risk. The analysis approach responds to four challenges facing researchers using in-vehicle data recorder data to assess driving behavior with surrogate safety performance measures: inclusion of exposure to risk; allowance for inclusion of driver, event, environment, and roadway attributes in a structured formulation; modeling of driving exposure in different contexts, including both roadway and environment; and allowance for comparison to a baseline hazard where no events occur. A hierarchical mixed-effects Poisson regression model is used with data from University of Michigan Transportation Institute's curve speed warning system field operation test in the application of the method. The model identifies drivers who have significantly higher alert frequency than similar drivers, both for all driving and for driving on major and minor arterials. The paper concludes with a discussion of potential applications beyond field operational test settings, which include analysis of crash and other naturalistic driving study-observed events.