mets (version 1.3.4)

twostageMLE: Twostage survival model fitted by pseudo MLE

Description

Fits Clayton-Oakes clustered survival data using marginals that are on Cox form in the likelihood for the dependence parameter as in Glidden (2000). The dependence can be modelled via a

1. Regression design on dependence parameter.

We allow a regression structure for the indenpendent gamma distributed random effects and their variances that may depend on cluster covariates. So $$ \theta = h( z_j^T \alpha) $$ where \(z\) is specified by theta.des . The link function can be the exp when var.link=1

Usage

twostageMLE(
  margsurv,
  data = parent.frame(),
  theta = NULL,
  theta.des = NULL,
  var.link = 0,
  method = "NR",
  no.opt = FALSE,
  weights = NULL,
  se.cluster = NULL,
  ...
)

Arguments

margsurv

Marginal model from phreg

data

data frame

theta

Starting values for variance components

theta.des

design for dependence parameters, when pairs are given this is could be a (pairs) x (numer of parameters) x (max number random effects) matrix

var.link

Link function for variance if 1 then uses exp link

method

type of opitmizer, default is Newton-Raphson "NR"

no.opt

to not optimize, for example to get score and iid for specific theta

weights

cluster specific weights, but given with length equivalent to data-set, weights for score equations

se.cluster

specifies how the influence functions are summed before squared when computing the variance. Note that the id from the marginal model is used to construct MLE, and then these scores can be summed with the se.cluster argument.

...

arguments to be passed to optimizer

Author

Thomas Scheike

References

Measuring early or late dependence for bivariate twin data Scheike, Holst, Hjelmborg (2015), LIDA

Twostage modelling of additive gamma frailty models for survival data. Scheike and Holst, working paper

Shih and Louis (1995) Inference on the association parameter in copula models for bivariate survival data, Biometrics, (1995).

Glidden (2000), A Two-Stage estimator of the dependence parameter for the Clayton Oakes model, LIDA, (2000).

Examples

data(diabetes)
dd <- phreg(Surv(time,status==1)~treat+cluster(id),diabetes)
oo <- twostageMLE(dd,data=diabetes)
summary(oo)

theta.des <- model.matrix(~-1+factor(adult),diabetes)

oo <-twostageMLE(dd,data=diabetes,theta.des=theta.des)
summary(oo)