One important connection for all human beings is that we have a shared need for health care and are at some risk of experiencing medical error. Errors documented in the field of radiation therapy have included tragic levels of suffering and even death from preventable mistakes. Efforts to improve patient safety include an approach called incident learning that makes use of information about known errors to inform safetyimprovement strategy. The complexity of human contributing factors challenges those safety improvements. The problem addressed by this dissertation study is that radiation therapy puts patients at risk, and the incident learning systems designed to inform safety improvements have yet to be optimized through a human factors framework. The Human Factors Analysis Classification System, a validated system utilized to categorize human contributing factors to error, was utilized in conjunction with a radiotherapy-specific list of distinct error types, such as treatment planning and quality assurance to classify a diverse international sample of radiotherapy safety events. The goal of this research was to discover predictive patterns of human factors contributing to radiotherapy incidents. Associations were uncovered between human contributing factors to error. Supervisory failures are linked to erroneous decision making and to unsafe environmental preconditions. Predictive associations between human factors and radiation therapy error types were discovered as well. Treatment-planning errors are associated with a specific kind of skill-based error that involves a lack of mindfulness. Quality assurance events are associated with certain supervisory and decision-type errors. Image-guidance errors are associated with perception failures and to failures at the human-computer interface. These associations incorporate a human factors framework and have direction for effective risk mitigation strategies.