Discrete Time-To-Event Modeling – Predicting When Something Will Happen

Time-to-event modeling, also known as survival analysis, focuses on predicting when an event will happen rather than what will happen. The approach requires deciding whether to treat time as discrete or continuous, depending on factors like event precision and data aggregation. A crucial concept is data censoring, especially right censoring, which occurs when an event has not yet happened for an observation or data collection has ceased. Failing to account for censoring introduces bias by underrepresenting the true event frequency. The life table is introduced as a foundational tool that addresses censoring by dividing time into discrete intervals to properly calculate event and survival probabilities.