kidneytran

Survival data of kidney transplant patients from Section 1.7
 of Klein and Moeschberger (2003).

datasets

Laplace approximations and penalized B-splines are combined
for fast Bayesian inference in latent Gaussian models. The routines can be
used to fit survival models, especially proportional hazards and promotion
time cure models (Gressani, O. and Lambert, P. (2018)
<doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
be implemented for inference in (generalized) additive models
(Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>).
See the associated website for more information and examples.

Oswaldo Gressani

blapsr

Bayesian Inference with Laplace Approximations and P-Splines

Philippe Lambert

kidneytran function

A data frame with 863 rows and 6 columns.<dl>
 <dt><code>obs</code></dt>
<dd>Observation number.</dd> <dt><code>time</code></dt>
<dd>Time to death.</dd> <dt><code>delta</code></dt>
<dd>Event indicator, <code>1</code>=Dead, <code>0</code>=Alive.</dd> <dt><code>gender</code></dt>
<dd>Gender, <code>1</code>=Male, <code>2</code>=Female.</dd> <dt><code>race</code></dt>
<dd>Race, <code>1</code>=White, <code>2</code>=Black.</dd> <dt><code>age</code></dt>
<dd>Age in years.</dd></dl>

Format

Survival data of kidney transplant patients. — kidneytran

A data frame with 863 rows and 6 columns.<dl>
 <dt><code>obs</code></dt>
<dd>Observation number.</dd>

 <dt><code>time</code></dt>
<dd>Time to death.</dd>

 <dt><code>delta</code></dt>
<dd>Event indicator, <code>1</code>=Dead, <code>0</code>=Alive.</dd>

 <dt><code>gender</code></dt>
<dd>Gender, <code>1</code>=Male, <code>2</code>=Female.</dd>

 <dt><code>race</code></dt>
<dd>Race, <code>1</code>=White, <code>2</code>=Black.</dd>

 <dt><code>age</code></dt>
<dd>Age in years.</dd>



</dl>

kidneytran: Survival data of kidney transplant patients.

Description

Usage

Arguments

Format

References