EVOLUTION-MANAGER
Edit File: survobrien.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: O'Brien's Test for Association of a Single Variable with...</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for survobrien {survival}"><tr><td>survobrien {survival}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2> O'Brien's Test for Association of a Single Variable with Survival </h2> <h3>Description</h3> <p>Peter O'Brien's test for association of a single variable with survival This test is proposed in Biometrics, June 1978. </p> <h3>Usage</h3> <pre> survobrien(formula, data, subset, na.action, transform) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>formula</code></td> <td> <p>a valid formula for a cox model. </p> </td></tr> <tr valign="top"><td><code>data</code></td> <td> <p>a data.frame in which to interpret the variables named in the <code>formula</code>, or in the <code>subset</code> and the <code>weights</code> argument. </p> </td></tr> <tr valign="top"><td><code>subset</code></td> <td> <p>expression indicating which subset of the rows of data should be used in the fit. All observations are included by default. </p> </td></tr> <tr valign="top"><td><code>na.action</code></td> <td> <p>a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is <code>options()\$na.action</code>. </p> </td></tr> <tr valign="top"><td><code>transform</code></td> <td> <p>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. </p> </td></tr></table> <h3>Value</h3> <p>a new data frame. The response variables will be column names returned by the <code>Surv</code> 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 <code>.strata.</code>. Other variables retain their original names. If a predictor variable is a factor or is protected with <code>I()</code>, it is retained as is. Other predictor variables have been replaced with time-dependent logit scores. </p> <p>The new data frame will have many more rows that the original data, approximately the original number of rows * number of deaths/2. </p> <h3>Method</h3> <p>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. </p> <p>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. </p> <h3>Note</h3> <p>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 <code>coxph</code> routine will process it slightly faster. </p> <h3>References</h3> <p>O'Brien, Peter, "A Nonparametric Test for Association with Censored Data", <em>Biometrics</em> 34: 243-250, 1978. </p> <h3>See Also</h3> <p><code><a href="survdiff.html">survdiff</a></code> </p> <h3>Examples</h3> <pre> xx <- survobrien(Surv(futime, fustat) ~ age + factor(rx) + I(ecog.ps), data=ovarian) coxph(Surv(time, status) ~ age + strata(.strata.), data=xx) </pre> <hr /><div style="text-align: center;">[Package <em>survival</em> version 2.44-1.1 <a href="00Index.html">Index</a>]</div> </body></html>