R: Missing data IPW Cox

cox.ipw {timereg} | R Documentation

## Missing data IPW Cox

### Description

Fits an Cox-Aalen survival model with missing data, with glm specification of probability of missingness.

### Usage

    cox.ipw(survformula, glmformula, d = sys.parent(), max.clust = NULL,
      ipw.se = FALSE, tie.seed = 100)

### Arguments

`survformula` | a formula object with the response on the left of a '~' operator, and the independent terms on the right as regressors. The response must be a survival object as returned by the ‘Surv’ function. Adds the prop() wrapper internally for using cox.aalen function for fitting Cox model.
`glmformula` | formula for "being" observed, that is not missing.
`d` | data frame.
`max.clust` | number of clusters in iid approximation. Default is all.
`ipw.se` | if TRUE computes standard errors based on iid decompositon of cox and glm model, thus should be asymptotically correct.
`tie.seed` | if there are ties these are broken, and to get same break the seed must be the same. Recommend to break them prior to entering the program.

### Details

Taylor expansion of Cox's partial likelihood in direction of glm parameters using num-deriv and iid expansion of Cox and glm paramters (lava).

### Value

returns an object of type "cox.aalen". With the following arguments:

`iid` | iid decomposition.
`coef` | missing data estiamtes for weighted cox.
`var` | robust pointwise variances estimates.
`se` | robust pointwise variances estimates.
`se.naive` | estimate of parametric components of model.
`ties` | list of ties and times with random noise to break ties.
`cox` | output from weighted cox model.

### Author(s)

Thomas Scheike

### References

Paik et al.

### Examples

    ### fit <- cox.ipw(Surv(time,status)~X+Z,obs~Z+X+time+status,data=d,ipw.se=TRUE)
    ### summary(fit)

[Package timereg version 1.9.4 Index]