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Survival analysis

In survival data the response variate is the survival time of an individual like a medical patient or an industrial component. The responses are often censored, i.e. some individuals survive beyond the end of the study, and so their survival times are unknown. Genstat provides various ways of estimating the survivor function (i.e. the probability that an individual is still surviving at each time). You can do nonparametric tests to compare different survival distributions. Finally, you can model the survival times, by assuming that they follow exponential, Weibull or extremevalue distributions, or by fitting a proportional hazards model.

    KAPLANMEIER calculates the Kaplan-Meier estimate of the survivor function
    RLIFETABLE calculates the life-table estimate of the survivor function
    RPHFIT fits the proportional hazards model to survival data as a generalized linear model
    RPHCHANGE modifies a proportional hazards model fitted by RPHFIT
    RPHDISPLAY prints output for a proportional hazards model fitted by RPHFIT
    RPHKEEP saves information from a proportional hazards model fitted by RPHFIT
    RPROPORTIONAL fits a proportional hazards model by a direct maximization of the likelihood (this will be more efficient than RPHFIT for large data sets)
    RSTEST compares groups of right-censored survival data by nonparametric tests
    RSURVIVAL models survival times of exponential, Weibull or extreme-value distributions
Updated on May 20, 2019

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