mHR2.tvc: Cox regression for a bivariate outcome with time-varying...

In mhazard: Nonparametric and Semiparametric Methods for Multivariate Failure Time Data

Cox regression for a bivariate outcome with time-varying covariates

Description

Fits a semiparametric Cox regression model for a bivariate outcome with time-varying covariates. Currently only the regression coefficients are computed.

Usage

mHR2.tvc(Y1, Y2, Delta1, Delta2, ids, X)

Arguments

Y1, Y2	Vectors of event times (continuous).
Delta1, Delta2	Vectors of censoring indicators (1=event, 0=censored).
ids	Vector of ID numbers. It is used to map the values of the time-varying covariates back to the original Y1/Y2/Delta1/Delta2 values. See Details.
X	Matrix of covariates (continuous or binary). See Details for the proper format of this matrix.

Value

A list containing the following elements:

beta10, beta01, beta11:

Regression coefficient estimates

Details

X must be a matrix with at least four columns. The first column contains the ID numbers. Each ID number in this column must map to a unique element of the ids vector. The second and third columns consists of time points for T1 and T2, respectively. They specify the time points at which the covariates take on the specified value(s). The remaining columns represent the values of the covariates on the specified time interval. For example, if we define X.tv <- matrix(c(1001, 1001, 0, 0, 0, 5, 1, 2), nrow=2) then, for the observation with ID number 1001, then when T1=0, the time-varying covariate has a value of 1 on when T2 is in [0,5) and a value of 2 when T2 is in [5,Inf). Note that the values of the time-varying covariates must be specified for when T1=0 (or T2=0) in order to compute beta10 and beta01. If a value of a covariate is constant when T1=0 or T2=0, that covariate will be dropped when computing beta10 or beta01.

Support for time-varying covariates is experimental and has not been tested extensively. Use this function at your own risk.

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

Examples

x <- genClaytonReg(50, 2, 0.5, 1, 1, 0, log(2), 0, 5, 5)
x.tv <- tvc.example(x$Y1, x$Y2, x$Delta1, x$Delta2, x$X)
x.mHR2 <- mHR2.tvc(x$Y1, x$Y2, x$Delta1, x$Delta2,
x.tv$ids, x.tv$X.tv)

mhazard documentation built on Aug. 17, 2023, 5:12 p.m.