summary.duplicate

Summarize results from fitting a logistic model with case duplication and
cluster-robust covariance.
The output is the same as for a regular <code>summary(glm(...))</code>,
except for using cluster-robust standard errors.

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

summary.duplicate function

<dl><dt>object</dt>
<dd>Model</dd>
<dt>dispersion</dt>
<dd>Not used</dd>
<dt>correlation</dt>
<dd>Not used</dd>
<dt>symbolic.cor</dt>
<dd>Not used</dd>
<dt>...</dt>
<dd>Other arguments, not used</dd></dl>

Arguments

Summary for logistic model with case duplication and cluster-robust covariance — summary.duplicate

<dl>

<dt>object</dt>
<dd>Model</dd>


<dt>dispersion</dt>
<dd>Not used</dd>


<dt>correlation</dt>
<dd>Not used</dd>


<dt>symbolic.cor</dt>
<dd>Not used</dd>


<dt>...</dt>
<dd>Other arguments, not used</dd>

</dl>

summary.duplicate: Summary for logistic model with case duplication and cluster-robust covariance

Description

Usage

Value

Arguments