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)
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
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).
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