leaveOneOut.survival2

For each subject, the survival function is recomputed leaving that subject out.
This is the workhorse for jackknife.survival2 and will generally not
be called by the user.

A user friendly, easy to understand way of doing event
history regression for marginal estimands of interest,
including the cumulative incidence and the restricted mean
survival, using the pseudo observation framework for
estimation. For a review of the methodology, see Andersen and
Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs
and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The
interface uses the well known formulation of a generalized
linear model and allows for features including plotting of
residuals, the use of sampling weights, and corrected
variance estimation.

Michael Sachs

eventglm

Regression Models for Event History Outcomes

Michael C Sachs

Erin E Gabriel

Morten Overgaard

Thomas A Gerds

Terry Therneau

leaveOneOut.survival2 function

<dl><dt>object</dt>
<dd>A survfit object, with a single event (no competing risks)</dd>
<dt>times</dt>
<dd>Times at which the survival is computed, must be length 1</dd>
<dt>mr</dt>
<dd>Model response, the result of a call to Surv, or a matrix with two columns: "time" (observed follow up time) and "status" (0 = censored, 1 = event)</dd></dl>

Arguments

Compute leave one out jackknife contributions of the survival function — leaveOneOut.survival2

<dl>

<dt>object</dt>
<dd>A survfit object, with a single event (no competing risks)</dd>


<dt>times</dt>
<dd>Times at which the survival is computed, must be length 1</dd>


<dt>mr</dt>
<dd>Model response, the result of a call to Surv, or a matrix with two columns: "time" (observed follow up time) and "status" (0 = censored, 1 = event)</dd>

</dl>

leaveOneOut.survival2: Compute leave one out jackknife contributions of the survival function

Description

Usage

Value

Arguments

Examples