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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 ...
The software also includes reference interval estimation, [9] meta-analysis and sample size calculations. The first DOS version of MedCalc was released in April 1993 and the first version for Windows was available in November 1996.
Epi Info is used for analysis in medical research, and for data entry. Examples of its use for research include a study of eye conditions, [6] a study of healthcare infections [7] and a study of psychiatric morbidity. [8] Examples of papers that used Epi Info for data entry include a study on nutrition [9] and an epidemiological survey about ...
This example of a survival tree analysis uses the R package "rpart". [8] The example is based on 146 stage C prostate cancer patients in the data set stagec in rpart. Rpart and the stagec example are described in Atkinson and Therneau (1997), [9] which is also distributed as a vignette of the rpart package. [8] The variables in stages are:
There are several software suites available to estimate relative survival rates. Regression modelling can be performed using maximum likelihood estimation methods by using Stata or R. [ 4 ] [ 5 ] For example, the R package cmprsk may be used for competing risk analyses which utilize sub-distribution or 'Fine and Gray' regression methods.
As a counterpart of the Kaplan–Meier curve, which is used to describe the time to a terminal event, recurrent event data can be described using the mean cumulative function, which is the average number of cumulative events experienced by an individual in the study at each point in time since the start of follow-up.
In full generality, the accelerated failure time model can be specified as [2] (|) = ()where denotes the joint effect of covariates, typically = ([+ +]). (Specifying the regression coefficients with a negative sign implies that high values of the covariates increase the survival time, but this is merely a sign convention; without a negative sign, they increase the hazard.)
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. A benefit of isotonic regression is that it is not constrained by any functional form, such as the linearity imposed by linear regression , as long as the ...