Abstract
Basic concepts of the modeling of discrete time-to-event data are considered. In particular the discrete hazard or intensity function, which represents the conditional probability that an event occurs at time t given it has not yet occurred, is introduced. Another feature that makes modeling time-to-event data special is censoring, which means that the exact time of the occurrence of an event is not known. Although it is natural to consider time as a continuous variable, observations frequently are on a discrete time scale, either because measurements are intrinsically discrete or because observations are available only in a grouped form. Several examples are given that are used in later chapters.
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Tutz, G., Schmid, M. (2016). Introduction. In: Modeling Discrete Time-to-Event Data. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-28158-2_1
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DOI: https://doi.org/10.1007/978-3-319-28158-2_1
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