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An example of a Kaplan–Meier plot for two conditions associated with patient survival. 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 ...
An example is the bathtub curve hazard function, which is large for small values of ... The Kaplan–Meier estimator can be used to estimate the survival function.
The graphs below show examples of hypothetical survival functions. The x-axis is time. The y-axis is the proportion of subjects surviving. The graphs show the probability that a subject will survive beyond time t. Four survival functions. For example, for survival function 1, the probability of surviving longer than t = 2 months is 0.37. That ...
For example, if the data from the two samples have exponential distributions. If is the logrank statistic, is the number of events observed, and ^ is the estimate of the hazard ratio, then ^ /. This relationship is useful when two of the quantities are known (e.g. from a published article), but the third one is needed.
Kaplan–Meier estimator [ edit ] The Dvoretzky–Kiefer–Wolfowitz inequality is obtained for the Kaplan–Meier estimator which is a right-censored data analog of the empirical distribution function
An early paper to use the Kaplan–Meier estimator for estimating censored costs was Quesenberry et al. (1989), [3] however this approach was found to be invalid by Lin et al. [4] unless all patients accumulated costs with a common deterministic rate function over time, they proposed an alternative estimation technique known as the Lin ...
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. I have moved the section to the end.
It can be used for example when testing the homogeneity of Poisson processes. [3] It was constructed by Wayne Nelson and Odd Aalen. [4] [5] [6] The Nelson-Aalen estimator is directly related to the Kaplan-Meier estimator and both maximize the empirical likelihood. [7]