In the study of health, many outcomes of interest are discrete. For example, does a patient have a disease or not? do they die from a disease or not? is the patient susceptible, infected, or recovered from an infectious disease? Similarly, categorical data are common in the study of behavior. For example, does the subject learn a trained task? Does a subject fight its competitor or not? Does the subject choose option a, b, or c in a choice test? A further commonality between behavior and health outcomes is that time is often an important aspect of the outcome. An epidemiologist might study whether or not a patient died and how long this took, whereas an ethologist may study whether and animal learned a task and how many trials this took. A final similarity is a lack of heterogeneity between subjects of interest. People from the same family or household may have a shared susceptibility to a disease event and animals’ from the same litter, or held in the same pen, may have a shared susceptibility to a behavioral event. Such similarities between types of data have resulted in some exploitation of similar statistical methods in both disciplines: random effects models, which account for unobserved heterogeneity, are common in both disciplines (1–4). However, statistical methods designed to study health-based events have been underutilized in behavior.