Fit a mixed effects Cox model
Description
Fit a Cox model containing mixed (random and fixed) effects. Assume a Gaussian distribution for the random effects.
Usage
1 2 3 4 
Arguments
formula 
a twosided formula with a survival object as the left hand side of a

data 
an optional data frame containing the variables named in the 
subset, weights, na.action 
further model specifications arguments as in 
init 
optional initial values for the fixed effects. 
control 
optional list of control options. See 
ties 
method for handling exact ties in the survival time. 
varlist 
the variance family to be used for each random term. If there are
multiple terms it will be a list of variance functions.
The default is 
vfixed 
optional named list or vector used to fix the value of one or more of the variance terms at a constant. 
vinit 
optional named list or vector giving suggested starting values for the variance. 
x 
if TRUE the X matrix (fixed effects) is included in the output object 
y 
if TRUE the y variable (survival time) is included in the output object 
refine.n 
number of samples to be used in a montecarlo estimate of possible error in the loglikelihood of the fitted model due to inadequacy of the Laplace approximation. 
fixed, random, variance 
In the preliminary version of 
... 
any other arguments are passed forward to 
Value
An object of class coxme
.
Author(s)
Terry Therneau
References
S Ripatti and J Palmgren, Estimation of multivariate frailty models using penalized partial likelihood, Biometrics, 56:10161022, 2000.
T Therneau, P Grambsch and VS Pankratz, Penalized survival models and frailty, J Computational and Graphical Statistics, 12:156175, 2003.
See Also
coxmeFull
, coxmeMlist
,
coxme.object
Examples
1 2 3 4 5 6 7 8 9 10  ## Not run: # Random treatment effects per institution
fit1 < coxme(Surv(pgtime, pgstat) ~ stage + trt + (1+trt institution),
data=colon2)
fit2 < coxme(Surv(pgtime, pgstat) ~ stage + trt + (trt institution) +
strata(institution), data=colon2)
## End(Not run)
# Shrinkage effects (equivalent to ridge regression)
temp < with(lung, scale(cbind(age, wt.loss, meal.cal)))
rfit < coxme(Surv(time, status) ~ ph.ecog + (temp  1), data=lung)
