mHR2 {mhazard}

Cox regression for a bivariate outcome

Description

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.

Usage

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).

Value

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

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

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)