Chap T. Le

Applied CDA 2e

• Broschiertes Buch

Produktbeschreibung

This new edition continues to provide basic, comprehensive coverage of key methods in categorical data analysis with multiple variables. Maintaining the same nontechnical, user-friendly approach, coverage has been added to the Second Edition to take the topic of categorical data analysis into a more applied direction. Coverage begins with an introduction to the basic methods and applications of contingency tables and loglinear models. Subsequent chapters examine analytical methods for matched data and count data, the Mantel-Haenszel method, Kappa statistics, ordinal risks, odds ratio estimates, goodness-of-fit, and various regression models for categorical data.

---

An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational research Thoroughly updated with the latest advances in the field, Applied Categorical Data Analysis and Translational Research, Second Edition maintains the accessible style of its predecessor while also exploring the importance of translational research as it relates to basic scientific findings within clinical practice. With its easy to follow style, updated coverage of major methodologies, and broadened scope of coverage, this new edition provides an accessible guide to statistical methods involving categorical data and the steps to their application in problem solving in the biomedical sciences. Delving even further into the applied direction, this update offers many real world examples from biomedicine, epidemiology, and public health along with detailed case studies taken straight from modern research in these fields. Additional features of the Second Edition include: A new chapter on the relationship between translational research and categorical data, focusing on design study, bioassay, and Phase I and Phase II clinical trials A new chapter on categorical data and diagnostic medicine, with coverage of the diagnostic process, prevalence surveys, the ROC function and ROC curve, and important statistical considerations A revised chapter on logistic regression models featuring an updated treatment of simple and multiple regression analysis An added section on quantal bioassays Each chapter features updated and new exercise sets along with numerous graphs that demonstrate the highly visual nature of the topic. A related Web site features the book s examples as well as additional data sets that can be worked with using SAS® software. The only book of its kind to provide balanced coverage of methods for both categorical data and translational research, Applied Categorical Data Analysis and Translational Research, Second Edition is an excellent book for courses on applied statistics and biostatistics at the upper undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the biomedical and public health fields.

Produktdetails

• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 416
• Erscheinungstermin: 1. Dezember 2009
• Englisch
• Abmessung: 234mm x 156mm x 22mm
• Gewicht: 564g
• ISBN-13: 9780470371305
• ISBN-10: 0470371307
• Artikelnr.: 28164785

Autorenporträt

Chap T. Le, PhD, is Distinguished Professor and Director of Biostatistics at the University of Minnesota Cancer Center. In addition to providing statistical consulting for a variety of biomedical research projects, he has worked on collaborations that have focused on the analyses of survival and categorical data and, currently, in the areas of cancer and tobacco research. Dr. Le is the author of Health and Numbers: A Problems-Based Introduction to Biostatistics, Third Edition; Introductory Biostatistics; and Applied Survival Analysis, all published by Wiley.

Inhaltsangabe

Preface. Preface to the First Edition. 1 Introduction. 1.1 A Prototype
Example. 1.2 A Review of Likelihood-Based Methods. 1.3 Interval Estimation
for a Proportion. 1.4 About This Book. 2 Contingency Tables. 2.1 Some
Sampling Models for Categorical Data. 2.1.1 The Binomial and Multinomial
Distributions. 2.1.2 The Hypergeometric Distributions. 2.2 Inferences for
2-by-2 Contingency Tables. 2.2.1 Comparison of Two Proportions. 2.2.2 Tests
for Independence. 2.2.3 Fisher's Exact Test. 2.2.4 Relative Risk and Odds
Ratio. 2.2.5 Etiologic Fraction. 2.2.6 Crossover Designs. 2.3 The
Mantel-Haenszel Method. 2.4 Inferences for General Two-Way Tables. 2.4.1
Comparison of Several Proportions. 2.4.2 Testing for Independence in
Two-Way Tables. 2.4.3 Ordered 2-by-k Contingency Tables. 2.5 Sample Size
Determination. Exercises. 3 Loglinear Models. 3.1 Loglinear Models for
Two-Way Tables. 3.2 Loglinear Models for Three-Way Tables. 3.2.1 The Models
of Independence. 3.2.2 Relationships Between Terms and Hierarchy of Models.
3.2.3 Testing a Specific Model. 3.2.4 Searching for the Best Model. 3.2.5
Collapsing Tables. 3.3 Loglinear Models for Higher-Dimensional Tables.
3.3.1 Testing a Specific Model. 3.3.2 Searching for the Best Model. 3.3.3
Measures of Association with an Effect Modification. 3.3.4 Searching for a
Model with a Dependent Variable. Exercises. 4 Logistic Regression Models.
4.1 Modeling a Probability. 4.1.1 The Logarithmic Transformation. 4.1.2 The
Probit Transformation. 4.1.3 The Logistic Transformation. 4.2 Simple
Regression Analysis. 4.2.1 The Logistic Regression Model. 4.2.2 Measure of
Association. 4.2.3 Tests of Association. 4.2.4 Use of the Logistic Model
for Different Designs. 4.2.5 Overdispersion. 4.3 Multiple Regression
Analysis. 4.3.1 Logistic Regression Model with Several Covariates. 4.3.2
Effect Modifications. 4.3.3 Polynomial Regression. 4.3.4 Testing Hypotheses
in Multiple Logistic Regression. 4.3.5 Measures of Goodness-of-Fit. 4.4
Ordinal Logistic Model. 4.5 Quantal Bioassays. 4.5.1 Types of Bioassays.
4.5.2 Quantal Response Bioassays. Exercises. 5 Methods for Matched Data.
5.1 Measuring Agreement. 5.2 Pair-Matched Case-Control Studies. 5.2.1 The
Model. 5.2.2 The Analysis. 5.2.3 The Case of Small Samples. 5.2.4 Risk
Factors with Multiple Categories and Ordinal Risks. 5.3 Multiple Matching.
5.3.1 The Conditional Approach. 5.3.2 Estimation of the Odds Ratio. 5.3.3
Testing for Exposure Effect. 5.3.4 Testing for Homogeneity. 5.4 Conditional
Logistic Regression. 5.4.1 Simple Regression Analysis. 5.4.2 Multiple
Regression Analysis. Exercises. 6 Methods for Count Data. 6.1 The Poisson
Distribution. 6.2 Testing Goodness-of-Fit. 6.3 The Poisson Regression
Model. 6.3.1 Simple Regression Analysis. 6.3.2 Multiple Regression
Analysis. 6.3.3 Overdispersion. 6.3.4 Stepwise Regression. Exercise. 7
Categorical Data and Translational Research. 7.1 Types of Clinical Studies.
7.2 From Bioassays to Translational Research. 7.2.1 Analysis of In Vitro
Experiments. 7.2.2 Design and Analysis of Experiments for Combination
Therapy. 7.3 Phase I Clinical Trials. 7.3.1 Standard Design. 7.3.2 Fast
Track Design. 7.3.3 Continual Reassessment Method. 7.4 Phase II Clinical
Trials. 7.4.1 Sample Size Determination for Phase II Clinical Trials. 7.4.2
Phase II Clinical Trial Designs for Selection. 7.4.3 Two-Stage Phase II
Design. 7.4.4 Toxicity Monitoring in Phase II Trials. 7.4.5 Multiple
Decisions. Exercises. 8 Categorical Data and Diagnostic Medicine. 8.1 Some
Examples. 8.2 The Diagnosis Process. 8.2.1 The Developmental Stage. 8.2.2
The Applicational Stage. 8.3 Some Statistical Issues. 8.3.1 The Response
Rate. 8.3.2 The Issue of Population Random Testing. 8.3.3 Screenable
Disease Prevalence. 8.3.4 An Index for Diagnostic Competence. 8.4
Prevalence Surveys. 8.4.1 Known Sensitivity and Specificity. 8.4.2 Unknown
Sensitivity and Specificity. 8.4.3 Prevalence Survey with a New Test. 8.5
The Receiver Operating Characteristic Curve. 8.5.1 The ROC Function and ROC
Curve. 8.5.2 Some Parametric ROC Models. 8.5.3 Estimation of the ROC Curve.
8.5.4 Index for Diagnostic Accuracy. 8.5.5 Estimation of Area Under ROC
Curve. 8.6 The Optimization Problem. 8.6.1 Basic Criterion: Youden's Index.
8.6.2 Possible Solutions. 8.7 Statistical Considerations. 8.7.1 Evaluation
of Screening Tests. 8.7.2 Comparison of Screening Tests. 8.7.3
Consideration of Subjects' Characteristics. Exercises. 9 Transition from
Categorical to Survival Data. 9.1 Survival Data. 9.2 Introductory Survival
Analysis. 9.2.1 Kaplan-Meier Curve. 9.2.2 Comparison of Survival
Distributions. 9.3 Simple Regression and Correlation. 9.3.1 Model and
Approach. 9.3.2 Measures of Association. 9.3.3 Tests of Association. 9.4
Multiple Regression and Correlation. 9.4.1 Proportional Hazards Models with
Several Covariates. 9.4.2 Testing Hypotheses in Multiple Regression. 9.4.3
Time-Dependent Covariates and Applications. 9.5 Competing Risks. 9.5.1
Redistribution to the Right Method. 9.5.2 Estimation of the Cumulative
Incidence. 9.5.3 Brief Discussion of Proportional Hazards Regression.
Exercise. Bibliography. Index.