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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.
Kaplan–Meier graph by treatment group in aml. The null hypothesis for a log-rank test is that the groups have the same survival. The expected number of subjects surviving at each time point in each is adjusted for the number of subjects at risk in the groups at each event 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.
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.
It can be thought of as the kaplan-meier survivor function for a particular year, divided by the expected survival rate in that particular year. That is typically known as the relative survival (RS). If five consecutive years are multiplied, the resulting figure would be known as cumulative relative survival (CRS). It is analogous to the five ...
Survival analysis includes Cox regression (Proportional hazards model) and Kaplan–Meier survival analysis. Procedures for method evaluation and method comparison include ROC curve analysis, [6] Bland–Altman plot, [7] as well as Deming and Passing–Bablok regression. [8]
Kaplan–Meier estimator; Kappa coefficient; Kappa statistic; Karhunen–Loève theorem; Kendall tau distance; Kendall tau rank correlation coefficient; Kendall's notation; Kendall's W – Kendall's coefficient of concordance; Kent distribution; Kernel density estimation; Kernel Fisher discriminant analysis; Kernel methods; Kernel principal ...
Stata utilizes integer storage types which occupy only one or two bytes rather than four, and single-precision (4 bytes) rather than double-precision (8 bytes) is the default for floating-point numbers. Stata's proprietary output language is known as SMCL, which stands for Stata Markup and Control Language and is pronounced "smickle". [10]