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The Kaplan–Meier estimator, [1] [2] also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. In medical research, it is often used to measure the fraction of patients living for a certain amount of time after treatment.
That is, 97% of subjects survive more than 2 months. Survival function 2. Median survival may be determined from the survival function: The median survival is the point where the survival function intersects the value 0.5. [4] For example, for survival function 2, 50% of the subjects survive 3.72 months. Median survival is thus 3.72 months.
This topic is called reliability theory, reliability analysis or reliability engineering in engineering, duration analysis or duration modelling in economics, and event history analysis in sociology. Survival analysis attempts to answer certain questions, such as what is the proportion of a population which will survive past a certain time?
Paul Meier (July 24, 1924 – August 7, 2011) [1] was a statistician who promoted the use of randomized trials in medicine. [2] [3]Meier is known for introducing, with Edward L. Kaplan, the Kaplan–Meier estimator, [4] [5] a method for measuring how many patients survive a medical treatment from one duration to another, taking into account that the sampled population changes over time.
Current: "[is] the individuals known to have survived and failed (the sum of individuals that have and have not yet had an event or been censored) up to time ."Suggested: "[is] the number of individuals at risk just before time (the number of individuals that have not yet had an event yet, including the censored ones, of course)."
Isotonic regression has applications in statistical inference.For example, one might use it to fit an isotonic curve to the means of some set of experimental results when an increase in those means according to some particular ordering is expected.
In mathematics, Meier function might refer to: Kaplan–Meier estimator; ... This page was last edited on 29 December 2019, ...
A given regression method will ultimately provide an estimate of , usually denoted ^ to distinguish the estimate from the true (unknown) parameter value that generated the data. Using this estimate, the researcher can then use the fitted value Y i ^ = f ( X i , β ^ ) {\displaystyle {\hat {Y_{i}}}=f(X_{i},{\hat {\beta }})} for prediction or to ...