survobrien {survival} | R Documentation |

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

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

`formula` |
a valid formula for a cox model. |

`data` |
a data.frame in which to interpret the variables named in
the |

`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 |

`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. |

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.

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.

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.

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

```
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 3.5-5 Index]