![]() , or by an overcomplicated method that uses a However, the censored data in each interval are handled either by an oversimplified method that assumes individuals with a censoring time in the second half of an interval survived that interval Furthermore, it directly estimates the survival probability, which is often of direct interest to patients and physicians than the hazard function in the Cox model. Compared to the Cox model, the discrete-time survival model is moreįlexible as it does not rely on the PH assumption. For each time interval, the conditional hazard/probability is estimated: the probability of failure in the interval, given that the individual has survived up to the beginning of the interval. ![]() In this modelling framework, the follow-up time is divided into a set of fixed intervals. Another school of deep neural network modelling for survival data utilized the discrete-time survival model, including recent papers by. ![]()
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