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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.
The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables.
An alternative to building a single survival tree is to build many survival trees, where each tree is constructed using a sample of the data, and average the trees to predict survival. [7] This is the method underlying the survival random forest models. Survival random forest analysis is available in the R package "randomForestSRC". [10]
The original SPSS manual (Nie, Bent & Hull, 1970) [11] has been described as one of "sociology's most influential books" for allowing ordinary researchers to do their own statistical analysis. [12] In addition to statistical analysis, data management (case selection, file reshaping and creating derived data) and data documentation (a metadata ...
Survival models relate the time that passes, before some event occurs, to one or more covariates that may be associated with that quantity of time. In a proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate .
The logrank test statistic compares estimates of the hazard functions of the two groups at each observed event time. It is constructed by computing the observed and expected number of events in one of the groups at each observed event time and then adding these to obtain an overall summary across all-time points where there is an event.
IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
SigmaStat can compare effects among groups, conduct survival analysis, analyze rates and proportions, perform regression and correlation analysis and calculate power and sample size. The program uses a wizard based interface which asks the user questions about the project and its data.
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