Nonparametric and Semiparametric Methods for Multivariate Failure Time Data
Generates survival data from a bivariate Clayton-Oakes model
Generates survival data from a trivariate Clayton-Oakes model
Generates regression data from a bivariate Clayton-Oakes model
Bivariate regression survival function and marginal hazards estimation
Cox regression for a bivariate outcome
Cox regression for a bivariate outcome with time-varying covariates
Nonparametric estimates of the survival function for bivariate failure...
Nonparametric estimates of the survival function for trivariate failur...
Uses a 3D perspective plot to visualize a nonparametric bivariate surv...
Uses a heat map to visualize a nonparametric bivariate survival functi...
Creates an example of a matrix of time-varying covariates
Nonparametric survival function estimates and semiparametric regression for the multivariate failure time data with right-censoring. For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.