aftgee {aftgee}  R Documentation 
LeastSquares Approach for Accelerated Failure Time with Generalized 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
aftgee(
formula,
data,
subset,
id = NULL,
contrasts = NULL,
weights = NULL,
margin = NULL,
corstr = c("independence", "exchangeable", "ar1", "unstructured", "userdefined",
"fixed"),
binit = "srrgehan",
B = 100,
control = aftgee.control()
)
Arguments
formula 
a formula expression, of the form 
data 
an optional data.frame in which to interpret the variables occurring
in the 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
id 
an optional vector used to identify the clusters.
If missing, then each individual row of 
contrasts 
an optional list. 
weights 
an optional vector of observation weights. 
margin 
a 
corstr 
a character string specifying the correlation structure. The following are permitted:

binit 
an optional vector can be either a numeric vector or a character string specifying the initial slope estimator.
The default value is "srrgehan". 
B 
a numeric value specifies the resampling number. When B = 0, only the beta estimate will be displayed. 
control 
controls maxiter and tolerance. 
Value
An object of class "aftgee
" representing the fit.
The aftgee
object 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
maxit
has been reached ini.conv
An integer code indicating type of convergence for initial value. 0 indicates successful convergence; 1 indicates that the iteration limit
maxit
has been reached conv.step
An integer code indicating the step until convergence
References
Chiou, S., Kim, J. and Yan, J. (2014) Marginal Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equation. Lifetime Data Analysis, 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
## Simulate data from an AFT model with possible depended response
datgen < function(n = 100, tau = 0.3, dim = 2) {
x1 < rbinom(dim * n, 1, 0.5)
x2 < rnorm(dim * n)
e < c(t(exp(MASS::mvrnorm(n = n, mu = rep(0, dim), Sigma = tau + (1  tau) * diag(dim)))))
tt < exp(2 + x1 + x2 + e)
cen < runif(n, 0, 100)
data.frame(Time = pmin(tt, cen), status = 1 * (tt < cen),
x1 = x1, x2 = x2, id = rep(1:n, each = dim))
}
set.seed(1); dat < datgen(n = 50, dim = 2)
fm < Surv(Time, status) ~ x1 + x2
fit1 < aftgee(fm, data = dat, id = id, corstr = "ind")
fit2 < aftgee(fm, data = dat, id = id, corstr = "ex")
summary(fit1)
summary(fit2)
confint(fit1)
confint(fit2)