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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.
The term reliability function is common in engineering while the term survival function is used in a broader range of applications, including human mortality. The survival function is the complementary cumulative distribution function of the lifetime.
In epidemiology, relative survival (as opposed to overall survival and associated with excess hazard rates) is defined as the ratio of observed survival in a population to the expected or background survival rate. [3] It can be thought of as the kaplan-meier survivor function for a particular year, divided by the expected survival rate in that ...
The Kaiser–Meyer–Olkin (KMO) test is a statistical measure to determine how suited data is for factor analysis.The test measures sampling adequacy for each variable in the model and the complete model.
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.
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.
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I beleive that an example calculation is necessary for a comprehensive description of the Kaplan-Meier estimate. However, I agree that the section is long, and it need not be in the middle of the article; it can be moved to the end for those readers who wish to see the example calculation.