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Biopharmaceutical Applied Statistics Symposium : Volume 2 Biostatistical Analysis of Clinical Trials.

By: Peace, Karl E.
Contributor(s): Chen, Ding-Geng | Menon, Sandeep.
Material type: TextTextSeries: eBooks on Demand.ICSA Book Series in Statistics Ser: Publisher: Singapore : Springer, 2018Copyright date: ©2018Description: 1 online resource (251 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9789811078262.Subject(s): Drugs-Testing-Congresses | Biopharmaceutics-CongressesGenre/Form: Electronic books.Additional physical formats: Print version:: Biopharmaceutical Applied Statistics Symposium : Volume 2 Biostatistical Analysis of Clinical TrialsDDC classification: 614.35 LOC classification: QA276-280Online resources: Click here to view this ebook.
Contents:
Intro -- Preface -- Contents -- Editors and Contributors -- 1 Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies -- 1.1 Introduction -- 1.2 Targeted Minimum Loss-Based Estimation -- 1.2.1 Inference -- 1.3 C-TMLE -- 1.3.1 Collaborative Double Robustness -- 1.3.2 Guiding Principles -- 1.3.3 Inference -- 1.4 C-TMLE Algorithms -- 1.4.1 Greedy C-TMLE -- 1.4.2 Scalable C-TMLE -- 1.5 Applications of C-TMLE in Health Care -- 1.5.1 Biomarker Discovery -- 1.5.2 Drug Safety -- 1.5.3 Future Work -- References -- 2 Generalized Tests in Clinical Trials -- 2.1 Introduction -- 2.2 Test Variables and Generalized p-Values -- 2.3 Generalized Confidence Intervals -- 2.4 Illustrations -- 2.5 Statistical Software -- Appendix -- References -- 3 Discrete Time-to-Event and Rank-Based Methods with Application to Composite Endpoint for Assessing Evidence of Disease Activity -- 3.1 Introduction -- 3.2 Data Structure -- 3.3 Analyses Methods -- 3.3.1 Collapsed Binary Composite Endpoint -- 3.3.2 Summary Estimate Across Studies -- 3.4 Example -- 3.5 Drawbacks of Collapsed Binary Composite Endpoint -- 3.6 Rank-Based Method -- 3.6.1 Ranking Binary Endpoints to Reflect Severity -- 3.6.2 Statistical Analysis of the Ranks -- 3.7 Concluding Remarks -- References -- 4 Imputing Missing Data Using a Surrogate Biomarker: Analyzing the Incidence of Endometrial Hyperplasia -- 4.1 Background -- 4.2 Objective of the Study -- 4.3 Validation -- 4.4 Analysis of Incidence of Endometrial Hyperplasia -- 4.5 Results for the Phase 3 Study -- Derivation -- References -- 5 Advancing Interpretation of Patient-Reported Outcomes -- 5.1 Introduction -- 5.2 Anchor-Based Approaches -- 5.2.1 Percentages Based on Thresholds -- 5.2.2 Criterion-Group Interpretation -- 5.2.3 Content-Based Interpretation -- 5.2.4 Clinically Important Difference.
5.2.5 Clinically Important Responder -- 5.3 Distribution-Based Approaches -- 5.3.1 Effect Size -- 5.3.2 Probability of Relative Benefit -- 5.3.3 Cumulative Distribution Functions -- 5.4 Mediation Models -- 5.4.1 Basic Elements -- 5.4.2 Basic Model -- 5.4.3 Example -- 5.5 Summary -- References -- 6 Network Meta-analysis -- 6.1 Introduction -- 6.2 Evidence Networks -- 6.3 Methodology and Application -- 6.3.1 Fixed-Effect Model -- 6.3.2 Random-Effects Model -- 6.3.3 Reporting and Interpreting -- 6.3.4 Application -- 6.4 Assumptions -- 6.4.1 Homogeneity -- 6.4.2 Similarity -- 6.4.3 Consistency -- 6.5 Special Topics -- 6.5.1 PRISMA Guidance -- 6.5.2 Individual Patient Data -- 6.5.3 Population-Adjusted Indirect Comparisons -- 6.6 Summary -- References -- 7 Detecting Safety Signals Among Adverse Events in Clinical Trials -- 7.1 Introduction -- 7.2 Sample Data -- 7.3 General Considerations for Safety Analyses -- 7.3.1 Initial Steps -- 7.3.2 Further Analyses -- 7.4 Safety Analysis of Sample Data -- 7.4.1 Initial Steps -- 7.4.2 Accounting for Time and Patient Exposure -- 7.4.3 Standardised MedDRA Queries -- 7.5 Conclusions -- References -- 8 Meta-analysis for Rare Events in Clinical Trials -- 8.1 Introduction -- 8.2 Overview of Meta-analysis -- 8.2.1 Summary Statistics and the Sources of Variations -- 8.2.2 Effect-Size Calculations for Binary Data -- 8.2.3 ES with Risk-Difference -- 8.2.4 Fixed-Effects Meta-analysis -- 8.2.5 Random-Effects Meta-analysis -- 8.3 Meta-analysis with Rare-Events -- 8.3.1 Potential Problems -- 8.3.2 The Rosiglitazone Meta-analysis -- 8.3.3 Step-by-Step Data Analysis in R -- 8.4 Discussion -- References -- 9 Missing Data -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Monotone Versus Non-monotone Missing Data -- 9.2.2 Missing Data Versus Missing Information -- 9.2.3 Notation -- 9.3 Analysis Model -- 9.4 A Model for Missingness.
9.5 Inverse Probability Weighting -- 9.6 Complete Data Model -- 9.7 It Is All About the Weighting -- 9.8 Treatment Contrast -- 9.9 Selection Model Factorization -- 9.10 Linear Mixed Model Repeated Measures Analysis -- 9.11 Generalized Linear Mixed Models -- 9.12 Multiple Imputation -- 9.13 Pattern Mixture Factorization -- 9.14 Discussion -- References -- 10 Bayesian Subgroup Analysis with Hierarchical Models -- 10.1 Introduction -- 10.2 Example -- 10.3 Bayesian Hierarchical Modeling for Subgroup Analysis -- 10.4 Empirical Bayes Subgroup Analysis -- 10.5 A More Fully Bayes Subgroup Analysis -- 10.6 Difference in Treatment Effect Between Subgroups -- 10.7 Effect Modifiers in Subgroup Analysis -- 10.8 Multi-way Bayesian Subgroup Analysis -- 10.9 Discussion -- Appendix -- References -- 11 A Question-Based Approach to the Analysis of Safety Data -- 11.1 Introduction -- 11.2 The Role of and a Need to Improve the Analysis and Reporting of Safety Data in Drug Development -- 11.3 The Nature of Safety Data and Core Safety Data Domains -- 11.3.1 The Nature of Safety Data -- 11.3.2 Core Safety Data Domains -- 11.4 Guidance on Analysis and Inference of Safety Data -- 11.4.1 Guidance on Analysis of Safety Data -- 11.4.2 Guidance on Statistical Inference of Safety Data -- 11.5 A Tiered Approach to the Analysis of Safety Data -- 11.6 Challenges in Reporting and Analysis of Safety Data -- 11.6.1 Reporting Safety Data -- 11.6.2 Analysis of Safey Data -- 11.7 A Question-Based Approach to the Analysis of Safety Data -- 11.7.1 A Question-Based Approach -- 11.7.2 Sample Questions -- 11.7.3 Examples -- 11.8 Conclusion -- References -- 12 Analysis of Two-Stage Adaptive Trial Designs -- 12.1 Introduction -- 12.2 Types of Two-Stage Adaptive Designs -- 12.3 Analysis SS Two-Stage Adaptive Designs -- 12.3.1 Theoretical Framework for Multiple-Stage Adaptive Designs.
12.3.2 Two-Stage Design -- 12.3.3 Conditional Power -- 12.4 Analysis SD Two-Stage Adaptive Designs -- 12.4.1 Continuous Endpoints -- 12.4.2 Binary Responses -- 12.4.3 Time-to-Event Endpoints -- 12.5 Analysis DS and DD Two-Stage Adaptive Designs -- 12.5.1 Non-adaptive Version -- 12.5.2 Adaptive Version -- 12.5.3 A Case Study of Hepatitis C Virus Infection -- 12.6 Concluding Remarks -- References -- Index.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QA276-280 (Browse shelf) https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=5497079 Available EBC5497079

Intro -- Preface -- Contents -- Editors and Contributors -- 1 Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies -- 1.1 Introduction -- 1.2 Targeted Minimum Loss-Based Estimation -- 1.2.1 Inference -- 1.3 C-TMLE -- 1.3.1 Collaborative Double Robustness -- 1.3.2 Guiding Principles -- 1.3.3 Inference -- 1.4 C-TMLE Algorithms -- 1.4.1 Greedy C-TMLE -- 1.4.2 Scalable C-TMLE -- 1.5 Applications of C-TMLE in Health Care -- 1.5.1 Biomarker Discovery -- 1.5.2 Drug Safety -- 1.5.3 Future Work -- References -- 2 Generalized Tests in Clinical Trials -- 2.1 Introduction -- 2.2 Test Variables and Generalized p-Values -- 2.3 Generalized Confidence Intervals -- 2.4 Illustrations -- 2.5 Statistical Software -- Appendix -- References -- 3 Discrete Time-to-Event and Rank-Based Methods with Application to Composite Endpoint for Assessing Evidence of Disease Activity -- 3.1 Introduction -- 3.2 Data Structure -- 3.3 Analyses Methods -- 3.3.1 Collapsed Binary Composite Endpoint -- 3.3.2 Summary Estimate Across Studies -- 3.4 Example -- 3.5 Drawbacks of Collapsed Binary Composite Endpoint -- 3.6 Rank-Based Method -- 3.6.1 Ranking Binary Endpoints to Reflect Severity -- 3.6.2 Statistical Analysis of the Ranks -- 3.7 Concluding Remarks -- References -- 4 Imputing Missing Data Using a Surrogate Biomarker: Analyzing the Incidence of Endometrial Hyperplasia -- 4.1 Background -- 4.2 Objective of the Study -- 4.3 Validation -- 4.4 Analysis of Incidence of Endometrial Hyperplasia -- 4.5 Results for the Phase 3 Study -- Derivation -- References -- 5 Advancing Interpretation of Patient-Reported Outcomes -- 5.1 Introduction -- 5.2 Anchor-Based Approaches -- 5.2.1 Percentages Based on Thresholds -- 5.2.2 Criterion-Group Interpretation -- 5.2.3 Content-Based Interpretation -- 5.2.4 Clinically Important Difference.

5.2.5 Clinically Important Responder -- 5.3 Distribution-Based Approaches -- 5.3.1 Effect Size -- 5.3.2 Probability of Relative Benefit -- 5.3.3 Cumulative Distribution Functions -- 5.4 Mediation Models -- 5.4.1 Basic Elements -- 5.4.2 Basic Model -- 5.4.3 Example -- 5.5 Summary -- References -- 6 Network Meta-analysis -- 6.1 Introduction -- 6.2 Evidence Networks -- 6.3 Methodology and Application -- 6.3.1 Fixed-Effect Model -- 6.3.2 Random-Effects Model -- 6.3.3 Reporting and Interpreting -- 6.3.4 Application -- 6.4 Assumptions -- 6.4.1 Homogeneity -- 6.4.2 Similarity -- 6.4.3 Consistency -- 6.5 Special Topics -- 6.5.1 PRISMA Guidance -- 6.5.2 Individual Patient Data -- 6.5.3 Population-Adjusted Indirect Comparisons -- 6.6 Summary -- References -- 7 Detecting Safety Signals Among Adverse Events in Clinical Trials -- 7.1 Introduction -- 7.2 Sample Data -- 7.3 General Considerations for Safety Analyses -- 7.3.1 Initial Steps -- 7.3.2 Further Analyses -- 7.4 Safety Analysis of Sample Data -- 7.4.1 Initial Steps -- 7.4.2 Accounting for Time and Patient Exposure -- 7.4.3 Standardised MedDRA Queries -- 7.5 Conclusions -- References -- 8 Meta-analysis for Rare Events in Clinical Trials -- 8.1 Introduction -- 8.2 Overview of Meta-analysis -- 8.2.1 Summary Statistics and the Sources of Variations -- 8.2.2 Effect-Size Calculations for Binary Data -- 8.2.3 ES with Risk-Difference -- 8.2.4 Fixed-Effects Meta-analysis -- 8.2.5 Random-Effects Meta-analysis -- 8.3 Meta-analysis with Rare-Events -- 8.3.1 Potential Problems -- 8.3.2 The Rosiglitazone Meta-analysis -- 8.3.3 Step-by-Step Data Analysis in R -- 8.4 Discussion -- References -- 9 Missing Data -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Monotone Versus Non-monotone Missing Data -- 9.2.2 Missing Data Versus Missing Information -- 9.2.3 Notation -- 9.3 Analysis Model -- 9.4 A Model for Missingness.

9.5 Inverse Probability Weighting -- 9.6 Complete Data Model -- 9.7 It Is All About the Weighting -- 9.8 Treatment Contrast -- 9.9 Selection Model Factorization -- 9.10 Linear Mixed Model Repeated Measures Analysis -- 9.11 Generalized Linear Mixed Models -- 9.12 Multiple Imputation -- 9.13 Pattern Mixture Factorization -- 9.14 Discussion -- References -- 10 Bayesian Subgroup Analysis with Hierarchical Models -- 10.1 Introduction -- 10.2 Example -- 10.3 Bayesian Hierarchical Modeling for Subgroup Analysis -- 10.4 Empirical Bayes Subgroup Analysis -- 10.5 A More Fully Bayes Subgroup Analysis -- 10.6 Difference in Treatment Effect Between Subgroups -- 10.7 Effect Modifiers in Subgroup Analysis -- 10.8 Multi-way Bayesian Subgroup Analysis -- 10.9 Discussion -- Appendix -- References -- 11 A Question-Based Approach to the Analysis of Safety Data -- 11.1 Introduction -- 11.2 The Role of and a Need to Improve the Analysis and Reporting of Safety Data in Drug Development -- 11.3 The Nature of Safety Data and Core Safety Data Domains -- 11.3.1 The Nature of Safety Data -- 11.3.2 Core Safety Data Domains -- 11.4 Guidance on Analysis and Inference of Safety Data -- 11.4.1 Guidance on Analysis of Safety Data -- 11.4.2 Guidance on Statistical Inference of Safety Data -- 11.5 A Tiered Approach to the Analysis of Safety Data -- 11.6 Challenges in Reporting and Analysis of Safety Data -- 11.6.1 Reporting Safety Data -- 11.6.2 Analysis of Safey Data -- 11.7 A Question-Based Approach to the Analysis of Safety Data -- 11.7.1 A Question-Based Approach -- 11.7.2 Sample Questions -- 11.7.3 Examples -- 11.8 Conclusion -- References -- 12 Analysis of Two-Stage Adaptive Trial Designs -- 12.1 Introduction -- 12.2 Types of Two-Stage Adaptive Designs -- 12.3 Analysis SS Two-Stage Adaptive Designs -- 12.3.1 Theoretical Framework for Multiple-Stage Adaptive Designs.

12.3.2 Two-Stage Design -- 12.3.3 Conditional Power -- 12.4 Analysis SD Two-Stage Adaptive Designs -- 12.4.1 Continuous Endpoints -- 12.4.2 Binary Responses -- 12.4.3 Time-to-Event Endpoints -- 12.5 Analysis DS and DD Two-Stage Adaptive Designs -- 12.5.1 Non-adaptive Version -- 12.5.2 Adaptive Version -- 12.5.3 A Case Study of Hepatitis C Virus Infection -- 12.6 Concluding Remarks -- References -- Index.

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