Applied Categorical Data Analysis and Translational Research, 2nd Edition
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.
Preface to the First Edition.
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.
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.
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.
4.3 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.
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.
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.
6.3.4 Stepwise Regression.
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.
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.
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.
- A new chapter focuses on the relationship between translational research and categorical data that is found in modenr practice, treating the topics of Phase I clinical trials, Phase II clinical trials, and bioassay.
A revised chapter on logistic regression models features updated treatment of simple and multiple regression analysis, along with new treatment of quantal bioassays as well as modeling a probability using PROBIT and other models
A new chapter on categorical data and diagnostic medicine takes this second edition, with coverage of the diagnosis process, prevalance surveys, the ROC Function and ROC Curve, and important statistical considerations.
New and revised exercises are included at the end of each chapter
- The author’s accessible, user-friendly writing style. combined with minimal use of mathematics, facilitates a basic comprehension of categorical data analysis in the biomedical field
- Real-world examples from the fields of epidemiology, biostatistics, and public health are employed throughout to illustrate modern applications to discussed method
- The inclusion of SAS codes has been maintained to assist readers with the analysis and interpretation of the presented data