survobrien {survival} | R Documentation

## O'Brien's Test for Association of a Single Variable with Survival

### Description

Peter O'Brien's test for association of a single variable with survival This test is proposed in Biometrics, June 1978.

### Usage

    survobrien(formula, data, subset, na.action, transform)

### Arguments

`formula` | a valid formula for a cox model.
`data` | a data.frame in which to interpret the variables named in the `formula`, or in the `subset` and the `weights` argument.
`subset` | expression indicating which subset of the rows of data should be used in the fit. All observations are included by default.
`na.action` | a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is `options()\$na.action`.
`transform` | the transformation function to be applied at each time point. The default is O'Brien's suggestion logit(tr) where tr = (rank(x)- 1/2)/ length(x) is the rank shifted to the range 0-1 and logit(x) = log(x/(1-x)) is the logit transform.

### Value

a new data frame. The response variables will be column names returned by the `Surv` function, i.e., "time" and "status" for simple survival data, or "start", "stop", "status" for counting process data. Each individual event time is identified by the value of the variable `.strata.`. Other variables retain their original names. If a predictor variable is a factor or is protected with `I()`, it is retained as is. Other predictor variables have been replaced with time-dependent logit scores.

The new data frame will have many more rows that the original data, approximately the original number of rows * number of deaths/2.

### Method

A time-dependent cox model can now be fit to the new data. The univariate statistic, as originally proposed, is equivalent to single variable score tests from the time-dependent model. This equivalence is the rationale for using the time dependent model as a multivariate extension of the original paper.

In O'Brien's method, the x variables are re-ranked at each death time. A simpler method, proposed by Prentice, ranks the data only once at the start. The results are usually similar.

### Note

A prior version of the routine returned new time variables rather than a strata. Unfortunately, that strategy does not work if the original formula has a strata statement. This new data set will be the same size, but the `coxph` routine will process it slightly faster.

### References

O'Brien, Peter, "A Nonparametric Test for Association with Censored Data", Biometrics 34: 243-250, 1978.

### See Also

`survdiff`

### Examples

    xx <- survobrien(Surv(futime, fustat) ~ age + factor(rx) + I(ecog.ps),
    			       data=ovarian)
    coxph(Surv(time, status) ~ age + strata(.strata.), data=xx)

[Package survival version 2.41-3 Index]