Fits a semiparametric Cox regression model for a bivariate outcome. This function computes the regression coefficients, baseline hazards, and sandwich estimates of the standard deviation of the regression coefficients. If desired, estimates of the survival function F and marginal hazard rates Lambda11 can be computed using the cox2.LF function.
cox2(Y1, Y2, Delta1, Delta2, X)
Vectors of event times (continuous).
Vectors of censoring indicators (1=event, 0=censored).
Matrix of covariates (continuous or binary).
A list containing the following elements:
Original vectors of event times
Original vectors of censoring indicators
Original covariate matrix
Total number of events for the first/second outcome
Total number of double events
Regression coefficient estimates
Baseline hazard estimates
Sandwich estimates of the standard deviation of the regression coefficients
Standard deviation estimates for the regression coefficients based on a univariate Cox model
Prentice, R., Zhao, S. "The statistical analysis of multivariate failure time data: A marginal modeling approach", CRC Press (2019). Prentice, R., Zhao, S. "Regression models and multivariate life tables", Journal of the American Statistical Association (2020) In press.
x <- genClaytonReg(1000, 2, 0.5, 1, 1, log(2), log(2), log(8/3), 2, 2) x.cox2 <- cox2(x$Y1, x$Y2, x$Delta1, x$Delta2, x$X)
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