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RESEARCH METHODOLOGY
Year : 2010  |  Volume : 1  |  Issue : 4  |  Page : 274-278

Understanding survival analysis: Kaplan-Meier estimate


1 Department of Community Medicine, Post Graduate Institute of Medical Science, Rohtak, Haryana, India
2 Department of Community Medicine, Maulana Azad Medical College, New Delhi, India

Correspondence Address:
Jugal Kishore
Department of Community Medicine, Maulana Azad Medical College, New Delhi - 110 002
India
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DOI: 10.4103/0974-7788.76794

PMID: 21455458

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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. 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. 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. 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. We label these situations as censored observations. 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. The survival curve can be created assuming various situations. 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. This can be calculated for two groups of subjects and also their statistical difference in the survivals. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.


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