survfit.cph

a fit object from <code>cph</code> or <code>coxph</code>
see <code><a rd-options="survival" href="/link/survfit.coxph?package=rms&version=6.0-0&to=survival" data-mini-rdoc="survival::survfit.coxph">survfit.coxph</a></code>

formula

see
 <code><a rd-options="survival" href="/link/survfit?package=rms&version=6.0-0&to=survival" data-mini-rdoc="survival::survfit">survfit</a></code>. If <code>individual</code> is <code>TRUE</code>,
 there must be exactly one <code>Surv</code> object in <code>newdata</code>. This 
 object is used to specify time intervals for time-dependent covariate
 paths. To get predictions for multiple subjects with time-dependent
 covariates, specify a vector <code>id</code> which specifies unique
 hypothetical subjects. The length of <code>id</code> should equal the
 number of rows in <code>newdata</code>.

newdata,se.fit,conf.int,individual,type,vartype,conf.type,id

&#8230;

This is a slightly modified version of Therneau's <code>survfit.coxph</code>
function. The difference is that <code>survfit.cph</code> assumes that
<code>x=TRUE,y=TRUE</code> were specified to the fit. This assures that the
environment in effect at the time of the fit (e.g., automatic knot
estimation for spline functions) is the same one used for basing predictions.

survival

Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  'rms' is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  'rms' works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.  Bayesian methods are implemented for binary and ordinal logistic regression with or without random effects.

Frank E Harrell Jr

Regression Modeling Strategies

survfit.cph function

a fit object from <code>cph</code> or <code>coxph</code>
see <code><a rd-options='survival' href='survfit.coxph'>survfit.coxph</a></code>

see
 <code><a rd-options='survival' href='survfit'>survfit</a></code>. If <code>individual</code> is <code>TRUE</code>,
 there must be exactly one <code>Surv</code> object in <code>newdata</code>. This 
 object is used to specify time intervals for time-dependent covariate
 paths. To get predictions for multiple subjects with time-dependent
 covariates, specify a vector <code>id</code> which specifies unique
 hypothetical subjects. The length of <code>id</code> should equal the
 number of rows in <code>newdata</code>.

survfit.cph: Cox Predicted Survival

Description

Usage

Arguments

Value

See Also