sex2

This data set deals with urinary tract infection in sexually active college women,
along with covariate information on age an contraceptive use.
The variables are all binary and coded in 1 (condition is present) and 0 (condition is absent).

datasets

Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys
prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary
maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available
in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.

Georg Heinze

logistf

Firth's Bias-Reduced Logistic Regression

Meinhard Ploner

Daniela Dunkler

Harry Southworth

Lena Jiricka

Gregor Steiner

sex2 function

sex2: a data.frame containing 239 observations<dl>
<dt>case</dt>
<dd>urinary tract infection, the study outcome variable</dd><dt>age</dt>
<dd>&gt;= 24 years</dd><dt>dia</dt>
<dd>use of diaphragm</dd><dt>oc</dt>
<dd>use of oral contraceptive</dd><dt>vic</dt>
<dd>use of condom</dd><dt>vicl</dt>
<dd>use of lubricated condom</dd><dt>vis</dt>
<dd>use of spermicide</dd>
</dl>

Format

Urinary Tract Infection in American College Students — sex2

sex2: a data.frame containing 239 observations<dl>
<dt>case</dt>
<dd>urinary tract infection, the study outcome variable</dd>

<dt>age</dt>
<dd>&gt;= 24 years</dd>

<dt>dia</dt>
<dd>use of diaphragm</dd>

<dt>oc</dt>
<dd>use of oral contraceptive</dd>

<dt>vic</dt>
<dd>use of condom</dd>

<dt>vicl</dt>
<dd>use of lubricated condom</dd>

<dt>vis</dt>
<dd>use of spermicide</dd>


</dl>

sex2: Urinary Tract Infection in American College Students

Description

Usage

Arguments

Format

References