breastcancer

<p>A cohort of women with breast cancer and complete follow-up, as used by
Spiegelman and Hertzmark (Am J Epidemiol 2005) and
Greenland (Am J Epidemiol 2004).</p>

datasets

Risk ratios and risk differences are estimated using regression
models that allow for binary, categorical, and continuous exposures and
confounders. Implemented are marginal standardization after fitting logistic
models (g-computation) with delta-method and bootstrap standard errors,
Miettinen's case-duplication approach (Schouten et al. 1993,
<doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical
variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting
values from Poisson models (Spiegelman and Hertzmark 2005,
<doi:10.1093/aje/kwi188>), and others.

Konrad Stopsack

risks

Estimate Risk Ratios and Risk Differences using Regression

Travis Gerke

breastcancer function


<div class="section" id="breastcancer">
<h3><code>breastcancer</code></h3><p></p><p>
</p><p>A tibble with 192 rows and 3 columns:</p><dl>
<dt>death</dt>
<dd><p>Death, binary: 0, 1</p></dd><p></p><p><dt>stage</dt>
<dd><p>Cancer stage, 3 categories</p></dd></p><p><dt>receptor</dt>
<dd><p>Hormone receptor status, binary: "High", "Low"</p></dd></p><p>...</p><p></p></dl><p></p><p></p></div>


Format

Breast Cancer Data — breastcancer


<div class='section' id='breastcancer'>
<h3><code>breastcancer</code></h3>


<p>A tibble with 192 rows and 3 columns:</p><dl>
<dt>death</dt>
<dd><p>Death, binary: 0, 1</p></dd>

<dt>stage</dt>
<dd><p>Cancer stage, 3 categories</p></dd>

<dt>receptor</dt>
<dd><p>Hormone receptor status, binary: "High", "Low"</p></dd>

...

</dl>

</div>


breastcancer: Breast Cancer Data

Description

Usage

Arguments

Format

`breastcancer`