In the health sciences, survival analysis is often used to model the time duration until the occurrence of an event—usually death (often referred to as the survival time). These survival time durations arise as a result of subjects being followed over time until they reach a specified endpoint or the event of interest occurs. An example of this is time to death of females who are diagnosed with breast cancer. Here, the event is death. Another example is the length of time a particular disease in humans remains in remission. The distribution of survival times are often skewed to the right and analysis often focuses on the probability that the individual survives for a given length of time. In time to event studies, subjects often leave the study either through death or are lost through follow-up or willingly leave the study. In other situations, some patients are not followed until death because of the expiration of the study at a specified time. Censoring occurs when an event of interest (e.g., remission, death, recovery) has not occurred by the time observations were made, so that all we knew at that point in time is that, the individual has survived at least up to some time). Thus censoring can not be glossed over as they carry important information about the factor of interest.