intcox (version 0.9.3)

intcox: Cox proportional hazards model for interval censored data

Description

Intcox fits the Cox proportional hazards model for interval censored data by the Iterative Convex Minorant Algorithm (ICM)

Usage

intcox(formula = formula(data), data = parent.frame(), subset, na.action, 
  x = FALSE, y = TRUE, epsilon = 1e-04, itermax = 10000, no.warnings = FALSE)

Arguments

formula

a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object of type "interval2" as returned by the Surv function.

data

a data.frame in which to interpret the variables named in the formula, or in the subset argument.

subset

expression saying that only a subset of the rows of the data should be used in the fit.

na.action

a missing-data filter function, applied to the model.frame, after any subset argument has been used. Default is options()$na.action.

x

Return the design matrix in the model object?

y

Return the response in the model object?

epsilon

convergence treshold. Iteration will continue until the relative change in the log-likelihood is less then epsilon. Default is .0001.

itermax

maximum number of iteration

no.warnings

logical value indicating how to handle warnings. If TRUE, warnings will be displayed. Default is FALSE.

Value

• an object of class "coxph". See coxph.object for details. Not all features are realised. Additionally there are given
• lambda0
  estimated baseline hazard
• time.point
  corresponding time points for the steps
• likeli.vec
  vector of the estimated loglik of each step
• termination
  indicator for the reason of termination, 1 - algorithm converged 2 - no improvement of likelihood possible, the iteration number is shown 3 - algorithm did not converge - maximum number of iteration reached 4 - inside precondition(s) are not fulfilled at this iteration

Details

With this package the Cox proportional hazards model can be applied for interval censored data. It tries to maximise the log-likelihood by a simultaneous improvement of the coefficients and the cumulative hazard function in the gradient direction weighted by the main diagonal elements of the negative Hessian matrix.

References

Wei Pan, (1999), Extending the Iterative Convex Minorant Algorithm to the Cox Model for Interval-Censored Data, Journal of Computational & Graphical Statistics, vol. 8, pp. 109-120

See Also

coxph, Surv

Examples

data(intcox.example)
intcox(Surv(left,right,type="interval2")~x.1+x.2+x.3+x.4,data=intcox.example)