Elsevier S & T

Handbook of Statistics: Epidemiology and Medical Statistics

Rao, C.R.

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Table of contents
  • Cover
  • Table of contentsv
  • Prefacexiii
  • Contributorsxv
  • Ch.1. Statistical Methods and Challenges in Epidemiology and Biomedical Research1
  • 1. Introduction1
  • 2. Characterizing the study cohort3
  • 3. Observational study methods and challenges6
  • 4. Randomized controlled trials12
  • 5. Intermediate, surrogate, and auxiliary outcomes17
  • 6. Multiple testing issues and high-dimensional biomarkers18
  • 7. Further discussion and the Women’s Health Initiative example21
  • References22
  • Ch.2. Statistical Inference for Causal Effects, With Emphasis on Applications in Epidemiology and Me28
  • 1. Causal inference primitives28
  • 2. The assignment mechanism36
  • 3. Assignment-based modes of causal inference41
  • 4. Posterior predictive causal inference47
  • 5. Complications55
  • References58
  • Ch.3. Epidemiologic Study Designs64
  • 1.Introduction64
  • 2.Experimental studies65
  • 3. Nonexperimental studies73
  • 4.Cohort studies73
  • 5. Case-control studies84
  • 6. Variants of the case-control design97
  • 7. Conclusion104
  • References104
  • Ch.4. Statistical Methods for Assessing Biomarkers and Analyzing Biomarker Data109
  • 1. Introduction109
  • 2. Statistical methods for assessing biomarkers110
  • 3. Statistical methods for analyzing biomarker data126
  • 4. Concluding remarks143
  • References144
  • Ch.5. Linear and Non-Linear Regression Methods in Epidemiology and Biostatistics148
  • 1. Introduction148
  • 2. Linear models151
  • 3. Non-linear models167
  • 4. Special topics176
  • References182
  • Ch.6. Logistic Regression187
  • 1. Introduction187
  • 2. Estimation of a simple logistic regression model188
  • 3. Two measures of model fit191
  • 4. Multiple logistic regression192
  • 5. Testing for interaction194
  • 6. Testing goodness of fit: Two measures for lack of fit195
  • 7. Exact logistic regression196
  • 8. Ordinal logistic regression201
  • 9. Multinomial logistic regression204
  • 10. Probit regression206
  • 11. Logistic regression in case-control studies207
  • References209
  • Ch.7. Count Response Regression Models210
  • 1. Introduction210
  • 2. The Poisson regression model212
  • 3. Heterogeneity and overdispersion224
  • 4. Important extensions of the models for counts230
  • 5. Software247
  • 6. Summary and conclusions250
  • References251
  • Ch.8. Mixed Models253
  • 1. Introduction253
  • 2. Estimation for the linear mixed model259
  • 3. Inference for the mixed model261
  • 4. Selecting the best mixed model264
  • 5. Diagnostics for the mixed model268
  • 6. Outliers270
  • 7. Missing data270
  • 8. Power and sample size272
  • 9. Generalized linear mixed models273
  • 10. Nonlinear mixed models274
  • 11. Mixed models for survival data275
  • 12. Software276
  • 13. Conclusions276
  • References277
  • Ch.9. Survival Analysis281
  • 1. Introduction281
  • 2. Univariate analysis282
  • 3. Hypothesis testing288
  • 4. Regression models295
  • 5. Regression models for competing risks310
  • References317
  • Ch.10. A Review of Statistical Analyses for Competing Risks321
  • 1. Introduction321
  • 2. Approaches to the statistical analysis of competing risks324
  • 3. Example327
  • 4. Conclusion339
  • References340
  • Ch.11. Cluster Analysis342
  • 1. Introduction342
  • 2. Proximity measures344
  • 3. Hierarchical clustering350
  • 4. Partitioning355
  • 5. Ordination (scaling)358
  • 6. How many clusters?361
  • 7. Applications in medicine364
  • 8. Conclusion364
  • References365
  • Ch.12. Factor Analysis and Related Methods367
  • 1. Introduction367
  • 2. Exploratory factor analysis (EFA)368
  • 3. Principle components analysis (PCA)375
  • 4. Confirmatory factor analysis (CFA)375
  • 5. FA with non-normal continuous variables379
  • 6. FA with categorical variables380
  • 7. Sample size in FA382
  • 8. Examples of EFA and CFA383
  • 9. Additional resources389
  • Appendix A: PRELIS and LISREL code for the CFA example with continuous MVs391
  • Appendix B: Mplus code for CFA example with categorical MVs391
  • References391
  • Ch.13. Structural Equation Modeling395
  • 1. Models and identification395
  • 2. Estimation and evaluation399
  • 3. Extensions of SEM410
  • 4. Some practical issues415
  • References418
  • Ch.14. Statistical Modeling in Biomedical Research: Longitudinal Data Analysis429
  • 1. Introduction429
  • 2. Analysis of longitudinal data431
  • 3. Design issues of a longitudinal study456
  • References460
  • Ch.15. Design and Analysis of Cross-Over Trials464
  • 1. Introduction464
  • 2. The two-period two-treatment cross-over trial467
  • 3. Higher-order designs476
  • 4. Analysis with non-normal data482
  • 5. Other application areas485
  • 6. Computer software488
  • References489
  • Ch.16. Sequential and Group Sequential Designs in Clinical Trials: Guidelines for Practitioners491
  • 1. Introduction492
  • 2. Historical background of sequential procedures493
  • 3. Group sequential procedures for randomized trials494
  • 4. Steps for GSD design and analysis507
  • 5. Discussion508
  • References509
  • Ch.17. Early Phase Clinical Trials: Phases I and II513
  • 1. Introduction513
  • 2. Phase I designs514
  • 3. Phase II designs526
  • 4. Summary539
  • References541
  • Ch.18. Definitive Phase III and Phase IV Clinical Trials546
  • 1. Introduction546
  • 2. Questions548
  • 3. Randomization550
  • 4. Recruitment551
  • 5. Adherence/sample size/power552
  • 6. Data analysis554
  • 7. Data quality and control/data management558
  • 8. Data monitoring558
  • 9. Phase IV trials563
  • 10. Dissemination – trial reporting and beyond564
  • 11. Conclusions565
  • References565
  • Ch.19. Incomplete Data in Epidemiology and Medical Statistics569
  • 1. Introduction569
  • 2. Missing-data mechanisms and ignorability571
  • 3. Simple approaches to handling missing data573
  • 4. Single imputation574
  • 5. Multiple imputation578
  • 6. Direct analysis using model-based procedures581
  • 7. Examples583
  • 8. Literature review for epidemiology and medical studies586
  • 9. Summary and discussion587
  • Appendix A588
  • Appendix B592
  • References598
  • Ch.20. Meta-Analysis602
  • 1. Introduction602
  • 2. History603
  • 3. The Cochran–Mantel–Haenszel test604
  • 4. Glass’s proposal for meta-analysis606
  • 5. Random effects models607
  • 6. The forest plot609
  • 7. Publication bias610
  • 8. The Cochrane Collaboration614
  • References614
  • Ch.21. The Multiple Comparison Issue in Health Care Research616
  • 1. Introduction616
  • 2. Concerns for significance testing617
  • 3. Appropriate use of significance testing618
  • 4. Definition of multiple comparisons619
  • 5. Rational for multiple comparisons620
  • 6. Multiple comparisons and analysis triage621
  • 7. Significance testing and multiple comparisons623
  • 8. Familywise error rate625
  • 9. The Bonferroni inequality626
  • 10. Alternative approaches629
  • 11. Dependent testing631
  • 12. Multiple comparisons and combined endpoints635
  • 13. Multiple comparisons and subgroup analyses641
  • 14. Data dredging651
  • References651
  • Ch.22. Power: Establishing the Optimum Sample Size656
  • 1. Introduction656
  • 2. Illustrating power658
  • 3. Comparing simulation and software approaches to power663
  • 4. Using power to decrease sample size672
  • 5. Discussion677
  • References677
  • Ch.23. Statistical Learning in Medical Data Analysis679
  • 1. Introduction679
  • 2. Risk factor estimation: penalized likelihood estimates681
  • 3. Risk factor estimation: likelihood basis pursuit and the LASSO690
  • 4. Classification: support vector machines and related estimates693
  • 5. Dissimilarity data and kernel estimates700
  • 6. Tuning methods704
  • 7. Regularization, empirical Bayes, Gaussian processes priors, and reproducing kernels707
  • References708
  • Ch.24. Evidence Based Medicine and Medical Decision Making712
  • 1. The definition and history of evidence based medicine712
  • 2. Sources and levels of evidence715
  • 3. The five stage process of EBM717
  • 4. The hierarchy of evidence: study design and minimizing bias718
  • 5. Assessing the significance or impact of study results: Statistical significance and confidence in721
  • 6. Meta-analysis and systematic reviews722
  • 7. The value of clinical information and assessing the usefulness of a diagnostic test722
  • 8. Expected values decision making and the threshold approach to diagnostic testing726
  • 9. Summary727
  • 10. Basic principles727
  • References728
  • Ch.25. Estimation of Marginal Regression Models with Multiple Source Predictors730
  • 1. Introduction730
  • 2. Review of the generalized estimating equations approach732
  • 3. Maximum likelihood estimation735
  • 4. Simulations737
  • 5. Efficiency calculations740
  • 6. Illustration741
  • 7. Conclusion743
  • References745
  • Ch.26. Difference Equations with Public Health Applications747
  • 1. Introduction747
  • 2. Generating functions748
  • 3. Second-order nonhomogeneous equations and generating functions750
  • 4. Example in rhythm disturbances752
  • 5. Follow-up losses in clinical trials758
  • 6. Applications in epidemiology765
  • References773
  • Ch.27. The Bayesian Approach to Experimental Data Analysis775
  • Preamble: and if you were a Bayesian without knowing it?775
  • 1. Introduction776
  • 2. Frequentist and Bayesian inference778
  • 3. An illustrative example783
  • 4. Other examples of inferences about proportions795
  • 5. Concluding remarks and some further topics803
  • References808
  • Subject Index813
  • Handbook of Statistics Contents of Previous Volumes823
Book details
  • Vendor Elsevier S & T
  • SKU 9780444528018
  • ISBN-13 9780080554211
  • Author Rao, C.R.
  • Category Social Science
  • Subject Statistics

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Description eBook delivery & access Reviews
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