leaveOneOut.survival

For each subject, the survival function is recomputed leaving that subject out.
This one does the calculation for all observed times, for calculation of the restricted mean

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.survival function

<dl><dt>object</dt>
<dd>A survfit object, with a single event (no competing risks)</dd>
<dt>times</dt>
<dd>Compute values at observed times up to and including this time</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.survival

<dl>

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


<dt>times</dt>
<dd>Compute values at observed times up to and including this time</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.survival: Compute leave one out jackknife contributions of the survival function

Description

Usage

Value

Arguments

Examples