| Edition |
2nd edition. |
| Description |
1 online resource (xi, 1458 pages) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Online resource; title from PDF title page (EBSCO, viewed July 13, 2015). |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
"The book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design, causal inference, personalized medicine to machine learning and next generation sequence analysis. Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible. Many distinguished scholars, who greatly advanced their research areas in statistical methodology as well as practical applications, also have revised several chapters with relevant updates and written new ones from scratch. The new edition has been divided into four sections, including, Statistical Methods in Medicine and Epidemiology, Statistical Methods in Clinical Trials, Statistical Genetics, and General Methods. To reflect the rise of modern statistical genetics as one of the most fertile research areas since the publication of the first edition, the brand new section on Statistical Genetics includes entirely new chapters reflecting the state of the art in the field. Although tightly related, all the book chapters are self-contained and can be read independently. The book chapters intend to provide a convenient launch pad for readers interested in learning a specific topic, applying the related statistical methods in their scientific research and seeking the newest references for in-depth research."-- Provided by publisher |
| Contents |
Preface to the Second Edition; Section 1. Statistics in Medicine and Epidemiology; Chapter 1. History of Statistical Thinking in Medicine; 1. Introduction; 2. Laplace and His Vision; 3. Louis and Numerical Method; 4. Statistical Analysis Versus Laboratory Investigation; 5. The Beginning of Modern Statistics; 6. The Beginning of Medical Statistics; 7. Randomization in Experimentation; 8. First Randomized Controlled Clinical Trial; 9. Government Regulation and Statistics; 10. Epilogue; Acknowledgment; References; About the Author |
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Chapter 2. Describing Data, Modeling Variation, and Statistical Practice1. Introduction; 2. Methods for Describing Data; 2.1. Data type and measurement; 2.1.1. Categorical data; 2.1.2. Continuous data; 2.1.3. Ratios; 2.1.4. Continuous proportional data; 2.1.5. Repeated measures; 2.1.6. Censored and truncated data; 2.2. Variability; 2.3. Basic techniques; 2.4. Graphic methods; 3. Describing Data via Adjusting for Factors with a Model; 4. Over-Dispersion Issues; 4.1. Over-dispersed binomial data; 4.2. Over-dispersed Poisson data; 4.3. Over-dispersed continuous proportional data |
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4.4. Modeling dispersion in continuous proportional data5. Statistical Practice; 5.1. Study design; 5.2. Research question; 5.3. Is the research goal explanatory or predictive?; 5.4. Data distribution, normality assumption and robustness; 5.5. Choosing the right statistical test; 5.5.1. Evaluating relationship; 5.5.2. Detecting group difference; 5.6. Constructing a model; 5.6.1. General steps; Acknowledgments; Appendix; References; About the Authors; Chapter 3. Covariate-Specific and Covariate-Adjusted Predictive Values of Prognostic Biomarkers with Survival Outcome; 1. Introduction |
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2. Covariate-Specific Time-Dependent PPV Curves2.1. Estimation of the conditional survival distribution based on the varying-coefficient Cox's model; 2.2. Semiparametric estimations of the conditional distribution of the biomarker and the covariate-specified time-dependent PPV curve; 2.3. Asymptotic properties; 3. Covariate-Adjusted Time-Dependent PPV Curve; 3.1. Estimation; 3.2. Asymptotic properties; 4. Simulation Studies; 4.1. Simulation study for covariate-specific time-dependent PPV curves; 4.2. Simulation study for covariate-adjusted time-dependent PPV curves |
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4.3. Comparison with the covariate-unadjusted time-dependent PPV curves5. Multicenter AIDS Cohort Study; 6. Discussion; Acknowledgment; References; About the Authors; Appendix A: Regular Conditions; Appendix B: Proof of Lemma 1 and Lemma 2; Appendix C: Proof of Theorems 1-2; Chapter 4. Statistical Methods for Personalized Medicine; 1. Introduction; 2. Personalized Risk Prediction; 2.1. Model building in personalized risk prediction; 2.2. Model evaluation in personalized risk prediction; 2.3. Additive value of biomarkers in personalized risk prediction |
| Subject |
Medical statistics.
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Medicine -- Research -- Statistical methods.
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Statistics as Topic -- methods |
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Biomedical Research -- methods |
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Médecine -- Recherche -- Méthodes statistiques.
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HEALTH & FITNESS -- Holism.
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HEALTH & FITNESS -- Reference.
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MEDICAL -- Alternative Medicine.
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MEDICAL -- Atlases.
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MEDICAL -- Essays.
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MEDICAL -- Family & General Practice.
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MEDICAL -- Holistic Medicine.
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MEDICAL -- Osteopathy.
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Medical statistics. (OCoLC)fst01014672
|
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Medicine -- Research -- Statistical methods.
(OCoLC)fst01015091
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| Added Author |
Lu, Ying, 1960- editor.
|
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Fang, Ji-Qian, 1939- editor.
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Tian, Lu, editor.
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Jin, Hua, 1967- editor.
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| Other Form: |
Print version: 9789814583312 (DLC) 2005274230 |
| ISBN |
9789814583312 (electronic bk.) |
|
9814583316 (electronic bk.) |
|
9789814583299 |
|
9814583294 |
|
9810247990 |
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9810248008 |
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9812388753 |
|
6611871756 |
|
1281871753 |
| Standard No. |
AU@ 000060908977 |
|
