Modeling, Confidence Intervals and Equivalence of Survival Curves
Parametric Bootstrap of time-to-event data following an exponential di...
Parametric Bootstrap of time-to-event data following a gaussian distri...
Parametric Bootstrap of time-to-event data following a logistic distri...
Parametric Bootstrap of time-to-event data following a loglogistic dis...
Parametric Bootstrap of time-to-event data following a lognormal distr...
Parametric Bootstrap of time-to-event data following a Weibull distrib...
Lower and upper confidence bounds for the difference of two parametric...
Lower and upper confidence bounds for the difference of two Kaplan-Mei...
Non-inferiority and equivalence test for the difference of two paramet...
Non-inferiority and equivalence test for the difference of two Kaplan-...
We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.