Special techniques for TTE data, as will be discussed below, have been developed to utilize the partial information on each subject with censored data and provide unbiased survival estimates. In the presence of censoring, the true time to event is underestimated. Traditional regression methods also are not equipped to handle censoring, a special type of missing data that occurs in time-to-event analyses when subjects do not experience the event of interest during the follow-up time. Traditional methods of logistic and linear regression are not suited to be able to include both the event and time aspects as the outcome in the model. Time-to-event (TTE) data is unique because the outcome of interest is not only whether or not an event occurred, but also when that event occurred. What is unique about time-to-event (TTE) data? This page briefly describes a series of questions that should be considered when analyzing time-to-event data and provides an annotated resource list for more information.
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