![]() ![]() Note in figure 2, censoring has occurred within the study (M) and terminally (L). Thus censoring can occur within the study or terminally at the end. This can happen when something negative for the study occurs, such as the subject drops out, is lost to follow-up, or required data is not available or, conversely, something good happens, such as the study ends before the subject had the event of interest occur, i.e., they survived at least until the end of the study, but there is no knowledge of what happened thereafter. Kaplan-Meier analyses are also used in non-medical disciplines.Ĭensoring means the total survival time for that subject cannot be accurately determined. As illustrated by these examples, “survival” times need not relate to actual survival with death being the event the “event” may be any event of interest. Examples of when times-to-events may be important end-point variables include cancer survival times, tympanostomy tube duration, onset times of hypocalcaemia following parathyroid resection, or duration of nasal congestion following septoplasty. 1 Subsequently, the Kaplan-Meier curves and estimates of survival data have become a familiar way of dealing with differing survival times (times-to-event), especially when not all the subjects continue in the study. Kaplan and Paul Meier collaborated to publish a seminal paper on how to deal with incomplete observations. These examples also illustrate the crucially important point that comparative analysis depends upon the whole curve and not upon isolated points. Two small groups of hypothetical data are used as examples in order for the reader to clearly see how the process works. Throughout this article we will discuss Kaplan-Meier (K-M) estimates in the context of “survival” before the event of interest. The purpose of this paper is to explain how Kaplan-Meier curves are generated and analyzed. Kaplan-Meier analyses are also used in non-medical disciplines. “Survival” times need not relate to actual survival with death being the event the “event” may be any event of interest. Subsequently, the Kaplan-Meier curves and estimates of survival data have become a familiar way of dealing with differing survival times (times-to-event), especially when not all the subjects continue in the study. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |