mHR2 function

Cox regression for a bivariate outcome

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 mHR2.LF function.

mHR2(Y1, Y2, Delta1, Delta2, X)

Arguments

• Y1, Y2: Vectors of event times (continuous).
• Delta1, Delta2: Vectors of censoring indicators (1=event, 0=censored).
• X: Matrix of covariates (continuous or binary).

Returns

A list containing the following elements:

• Y1, Y2:: Original vectors of event times
• Delta1, Delta2:: Original vectors of censoring indicators
• X:: Original covariate matrix
• n10, n01:: Total number of events for the first/second outcome
• n11:: Total number of double events
• beta10, beta01, beta11:: Regression coefficient estimates
• lambda10, lambda01, lambda11:: Baseline hazard estimates
• SD.beta10, SD.beta01, SD.beta11:: Sandwich estimates of the standard deviation of the regression coefficients
• SD.beta10.cox, SD.beta01.cox:: Standard deviation estimates for the regression coefficients based on a univariate Cox model

Examples

x <- genClaytonReg(1000, 2, 0.5, 1, 1, log(2), log(2), log(8/3), 2, 2)
x.mHR2 <- mHR2(x$Y1, x$Y2, x$Delta1, x$Delta2, x$X)

References

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 (2021) 116(535): 1330-1345. https://doi.org/10.1080/01621459.2020.1713792

See Also

mHR2.LF