survival.twostageCC: Twostage survival model for multivariate survival data
In mets: Analysis of Multivariate Event Times

survival.twostageCC

R Documentation

Twostage survival model for multivariate survival data

Description

older extended version of survival.twostage with more options and different maximizer.

Usage

survival.twostageCC(
  margsurv,
  data = parent.frame(),
  method = "nr",
  Nit = 60,
  detail = 0,
  clusters = NULL,
  silent = 1,
  weights = NULL,
  control = list(),
  theta = NULL,
  theta.des = NULL,
  var.link = 1,
  baseline.iid = 1,
  step = 0.5,
  model = "clayton.oakes",
  marginal.trunc = NULL,
  marginal.survival = NULL,
  marginal.status = NULL,
  strata = NULL,
  se.clusters = NULL,
  numDeriv = 0,
  random.design = NULL,
  pairs = NULL,
  dim.theta = NULL,
  numDeriv.method = "simple",
  additive.gamma.sum = NULL,
  var.par = 1,
  cr.models = NULL,
  case.control = 0,
  ascertained = 0,
  shut.up = 0
)

Arguments

`margsurv`	Marginal model
`data`	data frame
`method`	Scoring method "nr", "nlminb", "optimize", "nlm"
`Nit`	Number of iterations
`detail`	Detail
`clusters`	Cluster variable
`silent`	Debug information
`weights`	Weights
`control`	Optimization arguments
`theta`	Starting values for variance components
`theta.des`	design for dependence parameters, when pairs are given the indeces of the theta-design for this pair, is given in pairs as column 5
`var.link`	Link function for variance: exp-link.
`baseline.iid`	to adjust for baseline estimation, using phreg function on same data.
`step`	Step size
`model`	model
`marginal.trunc`	marginal left truncation probabilities
`marginal.survival`	optional vector of marginal survival probabilities
`marginal.status`	related to marginal survival probabilities
`strata`	strata for fitting, see example
`se.clusters`	for clusters for se calculation with iid
`numDeriv`	to get numDeriv version of second derivative, otherwise uses sum of squared score
`random.design`	random effect design for additive gamma model, when pairs are given the indeces of the pairs random.design rows are given as columns 3:4
`pairs`	matrix with rows of indeces (two-columns) for the pairs considered in the pairwise composite score, useful for case-control sampling when marginal is known.
`dim.theta`	dimension of the theta parameter for pairs situation.
`numDeriv.method`	uses simple to speed up things and second derivative not so important.
`additive.gamma.sum`	for two.stage=0, this is specification of the lamtot in the models via a matrix that is multiplied onto the parameters theta (dimensions=(number random effects x number of theta parameters), when null then sums all parameters.
`var.par`	is 1 for the default parametrization with the variances of the random effects, var.par=0 specifies that the λ_j's are used as parameters.
`cr.models`	competing risks models for two.stage=0, should be given as a list with models for each cause
`case.control`	assumes case control structure for "pairs" with second column being the probands, when this options is used the twostage model is profiled out via the paired estimating equations for the survival model.
`ascertained`	if the pair are sampled only when there is an event. This is in contrast to case.control sampling where a proband is given. This can be combined with control probands. Pair-call of twostage is needed and second column of pairs are the first jump time with an event for ascertained pairs, or time of control proband.
`shut.up`	to make the program more silent in the context of iterative procedures for case-control