Accelerated Failure Time with Generalize Estimating Equation
Description
Fits a semiparametric accelerated failure time (AFT) model with leastsquares approach. Generalized estimating equation is generalized to multivariate AFT modeling to account for multivariate dependence through working correlation structures to improve efficiency.
Usage
1 2 3 4 5 
Arguments
formula 
a formula expression, of the form 
data 
an optional data frame in which to interpret the variables
occurring in the 
id 
an optional vector used to identify the clusters.
If missing, then each individual row of 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
corstr 
a character string specifying the correlation structure.
The following are permitted:

B 
a numeric value specifies the resampling number. When B = 0, only the beta estimate will be displayed. 
contrasts 
an optional list. 
binit 
can be either a vector or a character string specifying
the initial slope estimator.
When 
weights 
an optional vector of observation weights. 
margin 
a sformula vector; default at 1. 
control 
controls maxiter and tolerance. 
Details
Package: aftgee Type: Package Version: 0.43 Date: 20140408 License: GPL (>=3) LazyLoad: yes 
Value
An object of class "aftgee" representing the fit. An object of class "aftgee" is a list containing at least the following components:
coefficients 
a vector of initial value and a vector of point estimates 
coef.res 
a vector of point estimates 
var.res 
estimated covariance matrix 
coef.init 
a vector of initial value 
var.init.mat 
estimated initial covariance matrix 
binit 
a character string specifying the initial estimator. 
conv 
An integer code indicating type of convergence after GEE
iteration. 0 indicates successful convergence; 1 indicates that the
iteration limit 
ini.conv 
An integer code indicating type of convergence for
initial value. 0 indicates successful convergence; 1 indicates that the
iteration limit 
conv.step 
An integer code indicating the step until convergence 
Author(s)
Sy Han Chiou, Sangwook Kang, Jun Yan.
References
Chiou, S., Kim, J. and Yan, J. (2014) Marginal Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equation. Life Time Data, 20(4): 599–618.
Jin, Z. and Lin, D. Y. and Ying, Z. (2006) On Leastsquares Regression with Censored Data. Biometrika, 90, 341–353.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  library(survival)
library(copula)
datgen < function(n = 100, tau = 0.3, cen = 75.4, dim = 2) {
kt < iTau(claytonCopula(1), tau)
copula < claytonCopula(kt, dim = dim)
id < rep(1:n, rep(dim, n))
x1 < rbinom(dim * n, 1, 0.5)
x2 < rnorm(dim * n)
ed < mvdc(copula, rep("weibull", dim), rep(list(list(shape = 1)), dim))
e < c(t(rMvdc(n, ed)))
T < exp(2 + x1 + x2 + e)
cstime < runif(n, 0, cen)
delta < (T < cstime) * 1
Y < pmin(T, cstime)
out < data.frame(T = T, Y = Y, delta = delta, x1 = x1, x2 = x2, id = rep(1:dim, n))
out
}
set.seed(1)
mydata < datgen(n = 50, dim = 2)
summary(aftgee(Surv(Y, delta) ~ x1 + x2, data = mydata,
id = id, corstr = "ind", B = 10))
summary(aftgee(Surv(Y, delta) ~ x1 + x2, data = mydata,
id = id, corstr = "ex", B = 10))

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