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.
Usage
cox2(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 (2020) In press.