| Description |
1 online resource (655 p.) |
| Series |
Chapman and Hall/CRC Handbooks of Modern Statistical Methods Ser |
|
Chapman and Hall/CRC Handbooks of Modern Statistical Methods Ser
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- List of Figures -- List of Tables -- Contributors -- I. Introduction to Randomized Controlled Trials -- 1. Introduction -- 1.1. Historical Background -- 1.2. Statistical Concepts -- 1.3. Organization of the Handbook -- Bibliography -- II. Analytic Methods for Randomized Controlled Trials -- 2. Binary and Ordinal Outcomes -- 2.1. Introduction -- 2.2. Analysis of 2 x 2 Contingency Tables -- 2.3. Analysis of R x C Contingency Tables -- 2.4. Analysis of Stratified 2 x 2 Contingency Tables |
|
2.5. Regression Models for Binary Outcomes -- 2.5.1. Logistic regression -- 2.5.2. Estimation and inference for logistic regression -- 2.5.3. Exact logistic regression -- 2.5.4. Example -- 2.6. Regression Models for Ordinal Outcomes -- 2.6.1. Proportional odds model -- 2.6.2. Some alternative models for ordinal outcomes -- 2.6.3. Example -- 2.7. Adjustment for Baseline Response -- 2.8. Concluding Remarks -- Bibliography -- 3. Continuous Outcomes -- 3.1. Introduction -- 3.2. The t-Test (One Population) -- 3.3. The t-Test (Two Populations) -- 3.4. Mann-Whitney U-Test -- 3.5. Paired Tests |
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3.5.1. Paired t-test -- 3.5.2. Wilcoxon signed rank test -- 3.6. Multiple Comparisons -- 3.7. Regression -- 3.7.1. Residuals -- 3.7.2. Inference for linear regression -- 3.7.3. ANCOVA models -- 3.7.4. Nonlinear regression -- 3.8. Conclusion -- Bibliography -- 4. Time to Event Data -- 4.1. Introduction -- 4.2. ACTG 320 -- 4.3. Mathematical Fundamentals -- 4.3.1. Notation -- 4.3.2. Hazard -- 4.3.3. Censoring and observed data -- 4.4. Estimation of Survival Distribution -- 4.5. Hypothesis Testing -- 4.6. Cox Regression Model -- 4.7. Informative Censoring -- 4.8. Conclusion -- Bibliography |
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5. Count Data -- 5.1. Introduction -- 5.2. Regression Analysis of Simple Count Data -- 5.2.1. Poisson regression for count -- 5.2.2. Negative binomial regression for count -- 5.2.3. Poisson and negative binomial regression for rate -- 5.2.4. Other models for simple count data -- 5.3. Regression Analysis of Correlated Count Data: Likelihood-Based Approaches -- 5.3.1. Maximum pseudo-likelihood estimation for the Poisson model -- 5.3.2. Maximum likelihood estimation for the Poisson model -- 5.3.3. Maximum likelihood estimation for the negative binomial model |
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5.4. Regression Analysis of Correlated Count Data: Distribution-Free Approaches -- 5.4.1. Conditional estimating equation method -- 5.4.2. Unconditional estimating equation method -- 5.4.3. Analysis of the National Cooperative Gallstone Study -- 5.5. Discussion and Concluding Remarks -- Bibliography -- 6. Longitudinal Data -- 6.1. Introduction -- 6.2. Generalized Linear Models -- 6.3. Generalized Estimating Equations -- 6.3.1. Notations -- 6.3.2. Asymptotic properties -- 6.3.3. Efficiency -- 6.3.4. Model selection criterion in GEE -- 6.4. Generalized Linear Mixed Models -- 6.4.1. Notations |
| Notes |
Description based upon print version of record |
|
6.4.2. Population average versus subject-specific model |
| Form |
Electronic book
|
| Author |
Bretz, Frank
|
|
Cheung, Ying Kuen K
|
|
Hampson, Lisa V
|
| ISBN |
9781498714648 |
|
1498714641 |
|
