proprate2 {surv2sample}R Documentation

Fitting the Two-Sample Proportional Rate Transformation Model for Censored Data

Description

proprate2 estimates the two-sample proportional rate transformation model (proportional hazards, proportional odds) for censored data using the simplified partial likelihood.

Usage

proprate2(x, group, model = 0, beta.init = 0, maxiter = 20,
          eps = 1e-09)

Arguments

x a "Surv" object, as returned by the Surv function.
group a vector indicating to which group each observation belongs. May contain values 1 and 2 only.
model the type of model. Possible values are 0 for proportional hazards, 1 for proportional odds.
beta.init the initial parameter value for iteration.
maxiter the maximum number of iterations.
eps the convergence tolerance parameter. The convergence criterion is |(l-l_old)/l|<eps.

Details

This function fits the proportional rate model for two samples of censored survival data. Currently two most important models are implemented: proportional hazards and proportional odds. The estimation procedure is based on a two-sample simplification of the partial for the two-sample situation, see Bagdonavicius and Nikulin (2000). (For proportional hazards, this method is the usual partial likelihood.)

Value

A list of class "proprate2.fit" with main components:

beta the estimate.
var its variance.
G0 the cumulative baseline rate (at times time).
time sorted times.
iter the number of iterations used.
converged logical. Did the iterations (appear to) converge?
loglik.init the simplified partial likelihood at the initial value of the parameter.
loglik the simplified partial likelihood at the estimate.
d11 the derivative of the score.
sigma11 variance of the score (for proportional hazards sigma11 equals d11).
G1, G2 cumulative transformation rates computed separately in the two groups (both of length n, at times time).

Author(s)

David Kraus (http://www.davidkraus.net/)

References

Bagdonavicius, V. and Nikulin, M. (2000) On goodness-of-fit for the linear transformation and frailty models. Statist. Probab. Lett. 47, 177–188.

Kraus, D. (2007) Checking proportional rates in the two-sample transformation model. Research Report 2203, Institute of Information Theory and Automation, Prague. Available at http://www.davidkraus.net/surv2sample/.

See Also

There is a plot method for objects returned by proprate2.

See proprate2.neyman, proprate2.ks, proprate2.gs for tests of the proportional rate assumption.

Examples

## chronic active hepatitis data
data(hepatitis)

## fit the proportional odds model
fit = with(hepatitis, proprate2(Surv(time, status), treatment,
    model = 1))
fit

## plot model-based and model-free estimates of odds functions
plot(fit)

[Package surv2sample version 0.1-2 Index]