<h2>
Applied Logistic Regression, 2nd Edition
</h2>


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<td> Authors: </td>
<td> David W. Hosmer, Jr., and Stanley Lemeshow </td>
</tr>
<tr>
<td> Publisher: </td>
<td> Wiley </td>
</tr>
<tr>
<td> Copyright: </td>
<td> 2000 </td>
</tr>
<tr>
<td> ISBN-10: </td>
<td> 0-471-35632-8 </td>
</tr>
<tr>
<td> ISBN-13: </td>
<td> 978-0-471-35632-5 </td>
</tr>
<tr>
<td> Pages: </td>
<td> 373; hardcover </td>
</tr>
<tr>
<td> Price: </td>
<td> $98.00 </td>
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<a href="http://www.stata.com/bookstore/alr.html#contents">Table of contents</a><br/>
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<h3>Comment from the Stata technical group</h3>

<p>
The second edition of <i>Applied Logistic Regression</i>, by David W. Hosmer
and Stanley Lemeshow, provides an excellent updated reference to the
advances in methodology in logistic regression that have taken place over
the last 10 years.
</p>

<p>
While the first edition has served as one of the few comprehensive
treatments of logistic regression available, the second edition introduces
many enhancements in the areas of assessing model fit, estimation using data
from complex survey samples, regression models for multinomial data, ordinal
data, and data with correlated responses.Also, the text now covers exact
tests and sample size calculations.
</p>

<p>
Many of the analyses in the book were performed in Stata and can be
replicated in Stata with data from the text.In particular, Stata’s
<b>svy:logit</b> command can be used to fit logistic regression models
with survey data.To download the data in Stata format, click
<a href="http://www.stata.com/bookstore/alr_dta.zip">here</a>.
</p>

<hr/>

<h3 id="contents">Table of contents</h3>

<div class="tier1">1. Introduction to the Logistic Regression Model</div>
<div class="tier2">
1.1 Introduction <br/>
1.2 Fitting the Logistic Regression Model <br/>
1.3 Testing for the Significance of the Coefficients <br/>
1.4 Confidence Interval Estimation <br/>
1.5 Other Methods of Estimation <br/>
1.6 Data Sets
<div class="tier3">
1.6.1 The ICU Study <br/>
1.6.2 The Low Birth Weight Study <br/>
1.6.3 The Prostate Cancer Study <br/>
1.6.4 The UMARU IMPACT Study
</div>
Exercises
</div>

<div class="tier1">2. Multiple Logistic Regression</div>
<div class="tier2">
2.1 Introduction <br/>
2.2 The Multiple Logistic Regression Model <br/>
2.3 Fitting the Multiple Logistic Regression Model <br/>
2.4 Testing for the Significance of the Model <br/>
2.5 Confidence Interval Estimation <br/>
2.6 Other Methods of Estimation <br/>
Exercises
</div>

<div class="tier1">3. Interpretation of the Fitted Logistic Regression Model</div>
<div class="tier2">
3.1 Introduction <br/>
3.2 Dichotomous Independent Variable <br/>
3.3 Polytomous Independent Variable <br/>
3.4 Continuous Independent Variable <br/>
3.5 The Multivariate Model<br/>
3.6 Interaction and Confounding <br/>
3.7 Estimation of Odds Ratios in the Presence of Interaction <br/>
3.8 A Comparison of Logistic Regression and Stratified Analysis for 2×2 Tables <br/>
Exercises
</div>

<div class="tier1">4. Model-Building Strategies and Methods for Logistic Regression</div>
<div class="tier2">
4.1 Introduction <br/>
4.2 Variable Selection <br/>
4.3 Stepwise Logistic Regression <br/>
4.4 Best Subsets Logistic Regression <br/>
4.5 Numerical Problems <br/>
Exercises
</div>

<div class="tier1">5. Assessing the Fit of the Model</div>
<div class="tier2">
5.1 Introduction <br/>
5.2 Summary Measures of Goodness-of-Fit
<div class="tier3">
5.2.1 Pearson Chi-Square Statistic and Deviance <br/>
5.2.2 The Hosmer–Lemeshow Tests <br/>
5.2.3 Classification Tables <br/>
5.2.4 Area Under the ROC Curve <br/>
5.2.5 Other Summary Measures
</div>
5.3 Logistic Regression Diagnostics <br/>
5.4 Assessment of Fit via External Validation <br/>
5.5 Interpretation and Presentation of Results from a Fitted Logistic Regression Model <br/>
Exercises
</div>

<div class="tier1">6. Application of Logistic Regression with Different Sampling Models</div>
<div class="tier2">
6.1 Introduction <br/>
6.2 Cohort Studies <br/>
6.3 Case-Control Studies <br/>
6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys <br/>
Exercises
</div>

<div class="tier1">7. Logistic Regression for Matched Case-Control Studies</div>
<div class="tier2">
7.1 Introduction<br/>
7.2 Logistic Regression Analysis for the 1-1 Matched Study <br/>
7.3 An Example of the Use of the Logistic Regression Model in a 1-1 Matched Study <br/>
7.4 Assessment of Fit in a 1-1 Matched Study <br/>
7.5 An Example of the Use of the Logistic Regression Model in a 1-<i>M</i> Matched Study <br/>
7.6 Methods for Assessment of Fit in a 1-<i>M</i> Matched Study <br/>
7.7 An Example of Assessment of Fit in a 1-<i>M</i> Matched Study <br/>
Exercises
</div>

<div class="tier1">8. Special Topics</div>
<div class="tier2">
8.1 The Multinomial Logistic Regression Model
<div class="tier3">
8.1.1 Introduction to the Model and Estimation of the Parameters <br/>
8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients <br/>
8.1.3 Model-Building Strategies for Multinomial Logistic Regression <br/>
8.1.4 Assessment of Fit and Diagnostics for the Multinomial Logistic Regression Model
</div>
8.2 Ordinal Logistic Regression Models
<div class="tier3">
8.2.1 Introduction to the Models, Methods for Fitting and Interpretation of Model Parameters <br/>
8.2.2 Model Building Models for the Analysis of Correlated Data
</div>
8.3 Logistic Regression Models for the Analysis of Correlated Data <br/>
8.4 Exact Methods for Logistic Regression Models <br/>
8.5 Sample Size Issues When Fitting Logistic Regression Models <br/>
Exercises
</div>

<div class="tier1">References</div>

<div class="tier1">Index</div>