Lung

Data from a randomised trial of two treatment regimens for lung cancer. This
is a standard survival analysis data set from the classic textbook by
Kalbfleisch and Prentice.

datasets

Fits regularization paths for linear regression, GLM, and Cox
regression models using lasso or nonconvex penalties, in particular the
minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)
penalty, with options for additional L2 penalties (the "elastic net" idea).
Utilities for carrying out cross-validation as well as post-fitting
visualization, summarization, inference, and prediction are also provided.
For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388>
or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.

Patrick Breheny

ncvreg

Regularization Paths for SCAD and MCP Penalized Regression
Models

Lung function

A list of two objects: <code>y</code> and <code>X</code><dl>
<dt>y</dt>
<dd>A two column matrix (<code>Surv</code> object) containing the follow-up
time (in days) and an indicator variable for whether the patient died
while on the study or not.</dd><dt>X</dt>
<dd>A matrix with 137 observations (rows) and 9 predictor variables
(columns). The remainder of this list describes the columns of <code>X</code></dd><dt>trt</dt>
<dd>Treatment indicator (1=control group, 2=treatment group)</dd><dt>karno</dt>
<dd>Karnofsky performance score (0=bad, 100=good)</dd><dt>diagtime</dt>
<dd>Time from diagnosis to randomization (months)</dd><dt>age</dt>
<dd>Age (years, at baseline)</dd><dt>prior</dt>
<dd>Prior therapy (0=no, 1=yes)</dd><dt>squamous</dt>
<dd>Indicator for whether the cancer type is squamous cell
carcinoma (0=no, 1=yes)</dd><dt>small</dt>
<dd>Indicator for whether the cancer type is small cell lung
cancer (0=no, 1=yes)</dd><dt>adeno</dt>
<dd>Indicator for whether the cancer type is adenocarcinoma
(0=no, 1=yes)</dd><dt>large</dt>
<dd>Indicator for whether the cancer type is large cell carcinoma
(0=no, 1=yes)</dd>
</dl>

Format

VA lung cancer data set — Lung

A list of two objects: <code>y</code> and <code>X</code><dl>
<dt>y</dt>
<dd>A two column matrix (<code>Surv</code> object) containing the follow-up
time (in days) and an indicator variable for whether the patient died
while on the study or not.</dd>

<dt>X</dt>
<dd>A matrix with 137 observations (rows) and 9 predictor variables
(columns). The remainder of this list describes the columns of <code>X</code></dd>

<dt>trt</dt>
<dd>Treatment indicator (1=control group, 2=treatment group)</dd>

<dt>karno</dt>
<dd>Karnofsky performance score (0=bad, 100=good)</dd>

<dt>diagtime</dt>
<dd>Time from diagnosis to randomization (months)</dd>

<dt>age</dt>
<dd>Age (years, at baseline)</dd>

<dt>prior</dt>
<dd>Prior therapy (0=no, 1=yes)</dd>

<dt>squamous</dt>
<dd>Indicator for whether the cancer type is squamous cell
carcinoma (0=no, 1=yes)</dd>

<dt>small</dt>
<dd>Indicator for whether the cancer type is small cell lung
cancer (0=no, 1=yes)</dd>

<dt>adeno</dt>
<dd>Indicator for whether the cancer type is adenocarcinoma
(0=no, 1=yes)</dd>

<dt>large</dt>
<dd>Indicator for whether the cancer type is large cell carcinoma
(0=no, 1=yes)</dd>


</dl>

Lung: VA lung cancer data set

Description

Usage

Arguments

Format

References

See Also