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coxme

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

coxme(formula, data, weights, subset, na.action, init, control,
ties = c("efron", "breslow"),
varlist, vfixed, vinit,  x = FALSE, y = TRUE,
refine.n = 0, random, fixed, variance, ...)

Arguments

`formula`	a two-sided formula with a survival object as the left hand side of a `~` operator and the fixed and random effects on the right.
`data`	an optional data frame containing the variables named in the `formula`.
`subset, weights, na.action`	further model specifications arguments as in `lm`; see there for details.
`init`	optional initial values for the fixed effects.
`control`	optional list of control options. See `coxme.control` for details.
`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 `coxmeFull`. Alternatively it can be a list of matrices, in which case the `coxmeMlist` function is used.
`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 monte-carlo estimate of possible error in the log-likelihood of the fitted model due to inadequacy of the Laplace approximation.
`fixed, random, variance`	In the preliminary version of `coxme` the fixed and random effects were separate arguments. These arguments are included for backwards compatability, but are depreciated. The variance argument is a depreciated alias for vfixed.
`...`	any other arguments are passed forward to `coxme.control`.

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, 56:1016-1022, 2000.

T Therneau, P Grambsch and VS Pankratz, Penalized survival models and frailty, J Computational and Graphical Statistics, 12:156-175, 2003.

Examples

# A non-significant institution effect
fit1 <- coxph(Surv(time, status) ~ ph.ecog + age, data=lung,
              subset=(!is.na(inst)))
fit2 <- coxme(Surv(time, status) ~ ph.ecog + age + (1|inst), lung)
anova(fit1, fit2)

# 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)

coxme

Mixed Effects Cox Models

v2.2-16

LGPL-2

Authors

Terry M. Therneau [aut, cre]

Initial release

coxme

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

coxme

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