a significant time has elapsed from the start. Sometimes we have subjects that become a part of the study later, i.e. We will know that they survived beyond a certain point, but we do not know the exact date of death. We do not want to ignore these subjects, because they provide some information about survival. We know that the event occurred (or will occur) sometime after the date of last follow-up. For these subjects we have partial information. We label these situations as right-censored observations. These analyses are often complicated when subjects under study are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experience or died, or we lose touch with them midway in the study. The time starting from a defined point to the occurrence of a given event is called as the survival time and the analysis of group data as the survival analysis. Sometimes it can even be used for a specific outcome, like how long it takes for a couple to conceive. In other situations, the duration for how long until a cancer relapses or how long until an infection occurs can be assessed. In many of the situations this length of time is very long for example in cancer therapy in such case per unit duration of time number of events such as death can be assessed. In situations where survival is the issue then the variable of interest would be the length of time that elapses before some event to occur. Sometime it is interesting to compare the survival of subjects in two or more interventions. In clinical or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.įor human subjects, to compare efficacy and safety, controlled experiments are conducted which are called as clinical trials. This can be calculated for two groups of subjects and also their statistical difference in the survivals. It involves computing of probabilities of occurrence of event at a certain point of time and multiplying these successive probabilities by any earlier computed probabilities to get the final estimate. The survival curve can be created assuming various situations. The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. We label these situations as censored observations. This can be affected by subjects under study that are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experienced or died if observation continued, or we lose touch with them midway in the study. The time starting from a defined point to the occurrence of a given event, for example death is called as survival time and the analysis of group data as survival analysis. In clinical trials or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment.