Handbook of Statistics Epidemiology and Medical Statistics 1st Edition by Rao, Philip Miller 0444528016 9780444528018

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ISBN 10: 0444528016

ISBN 13: 9780444528018

Author: C.R. Rao, J. Philip Miller, D.C. Rao

This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the development of new methods and innovative adaptations of standard methods. Since the literature is highly scattered, the Editors have undertaken this humble exercise to document a representative collection of topics of broad interest to diverse users. The volume spans a cross section of standard topics oriented toward users in the current evolving field, as well as special topics in much need which have more recent origins. This volume was prepared especially keeping the applied statisticians in mind, emphasizing applications-oriented methods and techniques, including references to appropriate software when relevant.

· Contributors are internationally renowned experts in their respective areas
· Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research
· Methods for assessing Biomarkers, analysis of competing risks
· Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs
· Structural equations modelling and longitudinal data analysis

Handbook of Statistics Epidemiology and Medical Statistics 1st Table of contents:

Ch.1. Statistical Methods and Challenges in Epidemiology and Biomedical Research

1. Introduction

2. Characterizing the study cohort

3. Observational study methods and challenges

4. Randomized controlled trials

5. Intermediate, surrogate, and auxiliary outcomes

6. Multiple testing issues and high-dimensional biomarkers

7. Further discussion and the Women’s Health Initiative example

References

Ch.2. Statistical Inference for Causal Effects, With Emphasis on Applications in Epidemiology and Me

1. Causal inference primitives

2. The assignment mechanism

3. Assignment-based modes of causal inference

4. Posterior predictive causal inference

5. Complications

References

Ch.3. Epidemiologic Study Designs

1.Introduction

2.Experimental studies

3. Nonexperimental studies

4.Cohort studies

5. Case-control studies

6. Variants of the case-control design

7. Conclusion

References

Ch.4. Statistical Methods for Assessing Biomarkers and Analyzing Biomarker Data

1. Introduction

2. Statistical methods for assessing biomarkers

3. Statistical methods for analyzing biomarker data

4. Concluding remarks

References

Ch.5. Linear and Non-Linear Regression Methods in Epidemiology and Biostatistics

1. Introduction

2. Linear models

3. Non-linear models

4. Special topics

References

Ch.6. Logistic Regression

1. Introduction

2. Estimation of a simple logistic regression model

3. Two measures of model fit

4. Multiple logistic regression

5. Testing for interaction

6. Testing goodness of fit: Two measures for lack of fit

7. Exact logistic regression

8. Ordinal logistic regression

9. Multinomial logistic regression

10. Probit regression

11. Logistic regression in case-control studies

References

Ch.7. Count Response Regression Models

1. Introduction

2. The Poisson regression model

3. Heterogeneity and overdispersion

4. Important extensions of the models for counts

5. Software

6. Summary and conclusions

References

Ch.8. Mixed Models

1. Introduction

2. Estimation for the linear mixed model

3. Inference for the mixed model

4. Selecting the best mixed model

5. Diagnostics for the mixed model

6. Outliers

7. Missing data

8. Power and sample size

9. Generalized linear mixed models

10. Nonlinear mixed models

11. Mixed models for survival data

12. Software

13. Conclusions

References

Ch.9. Survival Analysis

1. Introduction

2. Univariate analysis

3. Hypothesis testing

4. Regression models

5. Regression models for competing risks

References

Ch.10. A Review of Statistical Analyses for Competing Risks

1. Introduction

2. Approaches to the statistical analysis of competing risks

3. Example

4. Conclusion

References

Ch.11. Cluster Analysis

1. Introduction

2. Proximity measures

3. Hierarchical clustering

4. Partitioning

5. Ordination (scaling)

6. How many clusters?

7. Applications in medicine

8. Conclusion

References

Ch.12. Factor Analysis and Related Methods

1. Introduction

2. Exploratory factor analysis (EFA)

3. Principle components analysis (PCA)

4. Confirmatory factor analysis (CFA)

5. FA with non-normal continuous variables

6. FA with categorical variables

7. Sample size in FA

8. Examples of EFA and CFA

9. Additional resources

Appendix A: PRELIS and LISREL code for the CFA example with continuous MVs

Appendix B: Mplus code for CFA example with categorical MVs

References

Ch.13. Structural Equation Modeling

1. Models and identification

2. Estimation and evaluation

3. Extensions of SEM

4. Some practical issues

References

Ch.14. Statistical Modeling in Biomedical Research: Longitudinal Data Analysis

1. Introduction

2. Analysis of longitudinal data

3. Design issues of a longitudinal study

References

Ch.15. Design and Analysis of Cross-Over Trials

1. Introduction

2. The two-period two-treatment cross-over trial

3. Higher-order designs

4. Analysis with non-normal data

5. Other application areas

6. Computer software

References

Ch.16. Sequential and Group Sequential Designs in Clinical Trials: Guidelines for Practitioners

1. Introduction

2. Historical background of sequential procedures

3. Group sequential procedures for randomized trials

4. Steps for GSD design and analysis

5. Discussion

References

Ch.17. Early Phase Clinical Trials: Phases I and II

1. Introduction

2. Phase I designs

3. Phase II designs

4. Summary

References

Ch.18. Definitive Phase III and Phase IV Clinical Trials

1. Introduction

2. Questions

3. Randomization

4. Recruitment

5. Adherence/sample size/power

6. Data analysis

7. Data quality and control/data management

8. Data monitoring

9. Phase IV trials

10. Dissemination – trial reporting and beyond

11. Conclusions

References

Ch.19. Incomplete Data in Epidemiology and Medical Statistics

1. Introduction

2. Missing-data mechanisms and ignorability

3. Simple approaches to handling missing data

4. Single imputation

5. Multiple imputation

6. Direct analysis using model-based procedures

7. Examples

8. Literature review for epidemiology and medical studies

9. Summary and discussion

Appendix A

Appendix B

References

Ch.20. Meta-Analysis

1. Introduction

2. History

3. The Cochran–Mantel–Haenszel test

4. Glass’s proposal for meta-analysis

5. Random effects models

6. The forest plot

7. Publication bias

8. The Cochrane Collaboration

References

Ch.21. The Multiple Comparison Issue in Health Care Research

1. Introduction

2. Concerns for significance testing

3. Appropriate use of significance testing

4. Definition of multiple comparisons

5. Rational for multiple comparisons

6. Multiple comparisons and analysis triage

7. Significance testing and multiple comparisons

8. Familywise error rate

9. The Bonferroni inequality

10. Alternative approaches

11. Dependent testing

12. Multiple comparisons and combined endpoints

13. Multiple comparisons and subgroup analyses

14. Data dredging

References

Ch.22. Power: Establishing the Optimum Sample Size

1. Introduction

2. Illustrating power

3. Comparing simulation and software approaches to power

4. Using power to decrease sample size

5. Discussion

References

Ch.23. Statistical Learning in Medical Data Analysis

1. Introduction

2. Risk factor estimation: penalized likelihood estimates

3. Risk factor estimation: likelihood basis pursuit and the LASSO

4. Classification: support vector machines and related estimates

5. Dissimilarity data and kernel estimates

6. Tuning methods

7. Regularization, empirical Bayes, Gaussian processes priors, and reproducing kernels

References

Ch.24. Evidence Based Medicine and Medical Decision Making

1. The definition and history of evidence based medicine

2. Sources and levels of evidence

3. The five stage process of EBM

4. The hierarchy of evidence: study design and minimizing bias

5. Assessing the significance or impact of study results: Statistical significance and confidence in

6. Meta-analysis and systematic reviews

7. The value of clinical information and assessing the usefulness of a diagnostic test

8. Expected values decision making and the threshold approach to diagnostic testing

9. Summary

10. Basic principles

References

Ch.25. Estimation of Marginal Regression Models with Multiple Source Predictors

1. Introduction

2. Review of the generalized estimating equations approach

3. Maximum likelihood estimation

4. Simulations

5. Efficiency calculations

6. Illustration

7. Conclusion

References

Ch.26. Difference Equations with Public Health Applications

1. Introduction

2. Generating functions

3. Second-order nonhomogeneous equations and generating functions

4. Example in rhythm disturbances

5. Follow-up losses in clinical trials

6. Applications in epidemiology

References

Ch.27. The Bayesian Approach to Experimental Data Analysis

Preamble: and if you were a Bayesian without knowing it?

1. Introduction

2. Frequentist and Bayesian inference

3. An illustrative example

4. Other examples of inferences about proportions

5. Concluding remarks and some further topics

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