Logistic regression can be adapted for survival analysis by modeling grouped event times to estimate parameters similar to those in proportional hazards models. This approach helps when analyzing intervals for event occurrences (Abbott, 1985).
Khanda Aziz
In survival analysis, logistic regression is mostly used to model binary outcomes, such as whether an event has occurred by a given period. Additionally, it is modified for competing risks, models, in which many events are possible, and discrete-time survival analysis by calculating the likelihood of an event in time intervals. Furthermore, logistic regression is linked to continuous-time data by variants such as the complementary log-log model, which approximates models like Cox proportional hazards.
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