دانلود کتاب Handbook of Statistical Methods for Randomized Controlled Trials

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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 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 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 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 6.4.2. Population average versus subject-specific model 6.4.3. Estimation procedures 6.4.3.1. Marginal likelihood 6.4.3.2. Conditional likelihood 6.5. Test Statistics Under Randomization 6.5.1. Notations 6.5.2. Score-type test for GEE under randomization 6.5.3. Score test for GLMMs under randomization 6.6. Handling Missing Data in Clinical Trials 6.6.1. Missing data in GEE 6.6.2. Missing data in GLMMs 6.7. Case Study Bibliography 7. Recurrent Events 7.1. Introduction 7.1.1. Recurrent event data 7.1.2. Data from a cystic fibrosis Trial 7.2. Notation and Model Formulation 7.2.1. Analysis considerations with recurrent event data 7.2.2. Methods based on rate and mean functions 7.2.3. Censoring, Likelihood, and Marginal Methods 7.2.4. Assessment based on exacerbations in cystic fibrosis 7.3. Sample Size Based on Proportional Rate Functions 7.3.1. Derivations under a negative binomial model 7.3.2. Illustrative sample size calculation 7.4. Other Considerations in Recurrent Event Analyses 7.4.1. Issues regarding causal inference 7.4.2. Marginal multivariate failure times models 7.4.3. Adaptive two-stage sample size estimation 7.4.4. Recurrent and terminal events 7.5. Discussion Acknowledgments Bibliography III. Design of Randomized Controlled Trials 8. Cross-Over Designs 8.1. Introduction 8.2. Some Examples 8.2.1. Example 1 : An AB/BA design 8.2.2. Example 2: A design in three treatments, three periods, and six sequences 8.2.3. Example 3: An incomplete blocks design with fewer periods than treatments 8.2.4. Example 4: A replicate cross-over design with more periods than treatments 8.2.5. Example 5: A replicate bioequivalence study comparing two formulations in four periods 8.3. General Considerations 8.3.1. Phase of drug development 8.3.2. Suitable indications 8.4. Issues in Analysis 8.4.1. Models for cross-over trials 8.4.2. Patient effects and variance structures 8.4.3. Carry-over effects 8.4.4. Residual degrees of freedom and error estimation 8.5. Examples of Analysis 8.5.1. Basic estimator approach 8.5.2. Two-sample t-test approach 8.5.3. Linear and mixed models 8.5.4. Testing for carry-over 8.5.5. 8.5.5. An unbiased estimate of the treatment effect 8.5.6. The two-stage procedure 8.6. Issues in Design 8.6.1. Choosing sequences 8.6.2. Other issues 8.6.3. Planning the sample size 8.7. N-of-1 trials 8.8. Conclusion 8.9. Further reading 8.10. Acknowledgement Bibliography 9. Factorial Designs 9.1. Introduction 9.2. Different Usages of Factorial Designs 9.2.1. Efficiency of confirmatory trials: Evaluation of more than one Intervention in a single study 9.2.2. Screening trials: Developing multicomponent interventions 9.2.3. Situations where factorial designs are not suitable 9.3. Full Factorial Designs: A Theoretical Background 9.4. Fractional Factorial Designs 9.5. Analysis Strategies 9.6. Follow-up Studies: Developing Multicomponent Interventions 9.7. Power and Sample Size Considerations 9.8. Discussion Bibliography 10. Cluster Randomized Designs 10.1. What is a Cluster Randomized Trial? 10.2. The Problem of Clustering 10.3. Summary Statistics 10.4. The Intra-Cluster Correlation Coefficient and the Design Effect 10.5. Baseline and Other Adjustments 10.6. Robust Standard Errors 10.7. Multilevel Modeling 10.8. Generalized Estimating Equations (GEE) Models 10.9. Stepped Wedge Designs 10.10. Sample Size Estimation 10.11. Practical Problems of Cluster Randomized Trials Bibliography 11. Randomization, Stratification, and Outcome-Adaptive Allocation 11.1. Introduction 11.2. Simple and Restricted Randomization 11.3. Stratified and Covariate-Adaptive Randomization 11.4. Outcome-Adaptive Randomization 11.5. Concluding Remarks Bibliography 12. Background to Sample Size Calculations 12.1. Introduction 12.2. Types of Trials 12.2.1. Parallel group trials 12.2.2. Cross-over trials 12.3. Continuous Outcomes 12.3.1. Superiority trials 12.3.1.1. Parallel group trials 12.3.1.2. Quick results 12.3.1.3. Worked example 1 12.3.1.4. Cross-over trials 12.3.1.5. Quick results 12.3.1.6. Worked example 2 12.3.2. Equivalence trials 12.3.2.1. Parallel group trials 12.3.2.2. Worked example 3 12.3.2.3. Cross-over trials 12.3.2.4. Worked example 4 12.3.3. Non-inferiority trials 12.3.3.1. Parallel group trials 12.3.3.2. Worked example 5 12.3.3.3. Cross-over trials 12.3.3.4. Worked example 6 12.4. Binary Outcomes 12.4.1. Superiority trials 12.4.1.1. Parallel group trials 12.4.1.2. Method 2 12.4.1.3. Worked example 7 12.4.1.4. Cross-over trials 12.4.1.5. Worked example 8 12.4.2. Equivalence trials 12.4.2.1. Parallel group trials 12.4.2.2. Worked example 9 12.4.2.3. Cross-over trials 12.4.3. Non-inferiority trials 12.4.3.1. Parallel group trials 12.4.3.2. Worked example 10 12.5. Final Remarks Bibliography 13. Sample Size Estimation and Power Analysis: Time to Event Data 13.1. Introduction 13.2. Methods for Sample Size Estimation and Power Analysis 13.2.1. Approaches relating to acquisition of events 13.2.2. Estimation of required number of events: no accounting of other design parameters 13.2.3. Estimation of required number of events: with accounting of other design parameters 13.3. Case Studies 13.3.1. Rare events with non-proportional hazard ratio 13.3.1.1. The study as designed 13.3.1.2. The study as it unfolded 13.3.1.3. Insights gleaned from the study 13.3.1.4. Alternative strategies 13.3.1.5. Alternative strategy example 13.3.2. An oncology study 13.3.3. A diabetes noninferiority study 13.4. Special Topics and Recent Developments 13.4.1. Treatment effects beyond hazard ratios 13.4.2. Sample size re-estimation Bibliography 14. Sample Size Estimation and Power Analysis: Longitudinal Data 14.1. Introduction 14.2. Generalized Estimating Equations (GEE) Method 14.2.1. Continuous outcome variable case 14.2.2. Binary outcome variable case 14.3. Power Analysis and Sample Size Estimation 14.3.1. Continuous outcome variable case 14.3.2. Binary outcome variable case 14.4. Modelling Missing Pattern and Correlation Structure 14.4.1. Missing pattern 14.4.2. Correlation structure 14.5. Examples 14.5.1. Labor pain study (Continuous outcome case) 14.5.2. Design of an RCT based on GENISOS (binary outcome case) 14.6. Discussions Bibliography IV. Monitoring of Randomized Controlled Trials 15. Group Sequential Methods 15.1. Group Sequential Methods 15.1.1. A unified framework 15.1.2. Boundaries 15.2. The Effect of Monitoring on Power 15.3. Futility/Stochastic Curtailment 15.4. Problems with Post-Trial Inference 15.5. Conclusions Bibliography 16. Sample Size Re-Estimation 16.1. Introduction 16.2. Nuisance Parameter Based Sample Size Re-Estimation 16.2.1. Sample size re-estimation for normal data 16.2.1.1. Motivating example 16.2.1.2. Statistical model and sample size re-estimation 16.2.1.3. Unblinded nuisance parameter estimation 16.2.1.4. Blinded nuisance parameter estimation 16.2.1.5. Comparison of sample size re-estimation procedures 16.2.2. Sample size re-estimation for count data 16.2.2.1. Motivating example 16.2.2.2. Negative binomial outcomes 16.2.3. Further issues and recent developments 16.2.3.1. Non-inferiority trials 16.2.3.2. Controlling the type I error rate 16.2.3.3. Size of the internal pilot study 16.2.3.4. Covariates 16.2.3.5. Other endpoints and more complex designs 16.2.3.6. Multi-arm trials 16.2.3.7. Incorporating historical data into the sample size re-estimation 16.3. Effect-Based Sample Size Re-Estimation 16.3.1. Controlling the type I error rate 16.3.2. Sample size adaptation 16.3.3. Further issues and recent developments 16.4. Discussion Acknowledgements Bibliography 17. Adaptive Designs 17.1. Introduction 17.2. General Principles 17.2.1. The combination testing principle 17.2.2. The closed testing principle 17.2.3. Adaptive designs for multiple hypotheses 17.2.4. Assessing the performance of an adaptive design 17.3. Treatment Arm Selection Designs 17.3.1. The procedure 17.3.2. Binary and survival endpoints 17.3.3. Case studies 17.4. Population Enrichment Designs 17.4.1. The procedure 17.4.2. Effect specification 17.4.3. Binary and survival endpoints 17.4.4. Case studies 17.5. Discussion and Further Developments Acknowledgment Bibliography V. Practical Issues in Analysis of Randomized Controlled Trials 18. Multiple Testing 18.1. Error Rates in Multiple Comparisons 18.2. Principles of Multiple Testing 18.2.1. Partitioning principle 18.2.2. Closed testing principle 18.3. A Simple Example 18.4. Shortcutting 18.4.1. Holm's method is a shortcut 18.4.2. Hochberg's method is also a shortcut 18.5. Paths in Decision-Making 18.5.1. Decision path respecting principle 18.5.2. A specific dose x endpoint example 18.6. Setting Priorities in Multiple Testing for Each Study 18.6.1. The graphical approach 18.7. Logical Relationships Among Parameters Tested 18.7.1. Logic induced in multiple test construction 18.7.2. Logic inherent in scientific parameters 18.8. Going Forward Bibliography 19. Subgroup Analysis 19.1. Introduction 19.2. Methods for Conducting Subgroup Analyses 19.2.1. Commonly used methods 19.2.2. Qualitative interaction 19.2.3. Graphical methods 19.2.4. Multivariate tests of interaction 19.3. Power Consideration of Subgroup Analysis 19.4. Subgroup Analysis Reporting and Interpretation 19.5. Final Remarks Bibliography 20. Competing Risks 20.1. Introduction 20.2. Cumulative Incidence Function in the Presence of Competing Risks 20.2.1. Cumulative incidence function 20.2.2. Estimation of CIF in the presence of competing risks 20.3. Testing for Differences between Cumulative Incidence Curves in the Presence of Competing Risks 20.3.1. Gray test 20.3.2. Estimation of Gray statistic 20.4. Competing Risks Regression Analysis 20.4.1. Cause-specific hazard regression model 20.4.2. Fine and Gray model 20.4.3. Klein and Andersen model 20.4.4. Remarks 20.5. Conclusion 20.6. Computing Tools Acknowledgements Bibliography 21. Joint Models for Longitudinal and Time to Event Data 21.1. Introduction 21.2. Illustrative Example 21.3. Joint Shared Random-Effect Models 21.3.1. Model definition for Gaussian markers 21.3.2. Model definition for discrete markers 21.3.3. Estimation 21.3.3.1. Likelihood 21.3.3.2. Bayesian estimation 21.3.3.3. Model diagnostic 21.3.4. Joint shared random-effect models for clinical trials 21.3.4.1. Distinguishing direct and indirect treatment effects 21.3.4.2. Incomplete data 21.4. Joint Latent Class Models 21.4.1. Model definition 21.4.2. Estimation 21.4.2.1. Likelihood 21.4.2.2. Model diagnostic 21.4.3. Joint latent class models for clinical trials 21.5. Conclusion and Recent Developments Acknowledgements Bibliography VI. Miscellaneous Topics in Randomized Controlled Trials 22. Design and Analysis Methods for Developing Personalized Treatment Rules 22.1. Introduction 22.2. Study Design 22.3. Analysis Techniques: Single Stage 22.4. Analysis Techniques: Multiple Stages 22.5. Related Topics 22.5.1. Variable selection 22.5.2. Multiple outcomes 22.5.3. DTRs for observational data 22.6. Conclusion Bibliography 23. Safety Evaluation in Clinical Trials 23.1. Introduction 23.2. Elements of a Systematic Approach to Clinical Trial Safety Data Evaluation 23.2.1. The program safety analysis plan (PSAP) 23.2.2. Facilitating combining data across studies, including planning meta-analyses (be prepared) 23.3. Approaches to Characterizing the Product Safety Profile 23.3.1. Known or pre-specified safety issues 23.3.1.1. Specific safety issues that should always be considered for all products 23.3.1.2. Product-specific adverse events of special interest (AESIs) 23.3.1.3. Adverse events not specified in advance 23.3.2. Data sources for safety evaluation including specific safety studies 23.4. Planning for Clinical Data Collection and Standardization 23.4.1. Definition of safety outcomes and adjudication 23.4.2. Standardization of safety data collection 23.5. Safety Data Analysis and Reporting 23.5.1. Considerations for individual studies 23.5.1.1. Defining the safety analysis set 23.5.1.2. Accounting for time on or off treatment 23.5.2. Meta-analysis of adverse event data 23.5.3. Multiplicity 23.5.4. Signal detection for common events 23.5.5. Descriptive analysis of infrequent adverse events 23.5.6. Reporting 23.6. Conclusions Bibliography 24. Non-Inferiority Trials 24.1. Background and History 24.2. Basics 24.2.1. Historical studies 24.2.2. Parameters and margins 24.2.3. Study design and conduct 24.2.4. Test statistics, confidence intervals and decision rules 24.2.5. Reporting and interpretation 24.2.6. Power and sample size assessment 24.2.7. Equivalence and non-inferiority 24.3. Issues and Evolving Ideas 24.3.1. Analysis sets 24.3.2. Missing data 24.3.3. Adaptive designs 24.4. Conclusions Bibliography 25. Incorporating Historical Data into Randomized Controlled Trials 25.1. Introduction 25.2. Case Study 25.3. Meta-Analytic-Predictive Approach 25.3.1. Hierarchical model 25.3.2. Mixture approximation for priors 25.3.3. Robustness to a prior-data conflict 25.3.4. Prior effective sample size 25.3.5. Operating characteristics 25.3.6. Analysis 25.4. Other Approaches 25.4.1. Meta-analytic-combined approach 25.4.2. Bias models 25.4.3. Commensurate priors 25.4.4. Power priors 25.4.5. Test-then-pool 25.4.6. How much borrowing? 25.5. Extensions 25.5.1. Individual patient data and aggregate data 25.5.2. Non-inferiority trials 25.6. Discussion 25.7. Appendix 25.7.1. WinBUGS code 25.7.2. SAS code Bibliography 26. Evaluation of Surrogate Endpoints 26.1. Introduction 26.2. Data from a Single Trial 26.2.1. Definition and criteria 26.2.2. The proportion explained 26.2.3. The relative effect 26.3. A Meta-analytic Framework for Normally Distributed Outcomes 26.3.1. A meta-analytic approach 26.4. Non-Gaussian Endpoints 26.4.1. Two binary endpoints 26.4.2. Two failure-time endpoints 26.4.3. An ordinal surrogate and a survival endpoint 26.4.4. Binary and normally distributed endpoints 26.4.5. Longitudinal endpoints 26.5. Alternatives and Extensions 26.6. Prediction and Design Aspects 26.7. Case Studies 26.7.1. A meta-analysis of five clinical trials in schizophrenia 26.7.1.1. Analysis of continuous endpoints 26.7.1.2. Analysis of the categorical endpoints 26.7.2. Prostate-specific antigen (PSA) 26.7.2.1. PSA as a surrogate in multiple trials 26.7.3. Surrogate endpoints in gastric cancer 26.7.3.1. Resectable gastric cancer: can DFS be used a surrogate for OS? 26.7.3.2. Advanced gastric cancer: can PFS be used as a surrogate for OS? 26.7.3.3. Contrasting conclusions about DFS and PFS 26.8. Concluding Remarks Acknowledgment Bibliography Index