Introduction to Statistical Methods for Clinical Trials 1st edition by Bradley Carlin, Thomas Cook, Julian Faraway, Jim Zidek, Martin Tanner, David DeMets ISBN 1584880279 978-1584880271

Introduction to Statistical Methods for Clinical Trials 1st edition by Bradley Carlin, Thomas Cook, Julian Faraway, Jim Zidek, Martin Tanner, David DeMets – Ebook PDF Instant Download/Delivery: 1584880279 , 978-1584880271
Full download Introduction to Statistical Methods for Clinical Trials 1st edition after payment

Product details:

ISBN 10: 1584880279
ISBN 13: 978-1584880271
Author: Bradley Carlin, Thomas Cook, Julian Faraway, Jim Zidek, Martin Tanner, David DeMets 

Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial.

After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals.

Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.

Introduction to Statistical Methods for Clinical Trials 1st Table of contents:

1 Introduction to Clinical Trials

1.1 History and Background

1.2 Ethics of Clinical Research

1.3 Types of Research Design and Types of Trials

1.4 The Need for Clinical Trials

1.5 The Randomization Principle

1.6 Timing of a Clinical Trial

1.7 Trial Organization

1.8 Protocol and Manual of Operations

1.9 Regulatory Issues

1.9.1 Institutional Review Board

1.9.2 Informed Consent Process Guidelines

1.9.3 Food and Drug Administration & Other Regulatory Agencies

1.10 Overview of the Book

2 Defining the Question

2.1 Statistical Framework

2.1.1 Causal Inference

2.1.2 The Intent-To-Treat Principle

2.2 Elements of Study Question

2.2.1 The Population

2.2.2 Primary and Secondary Questions

2.2.3 Subgroup Questions

2.2.4 Safety Questions

2.3 Outcome or Response Measures

2.3.1 Analysis of Outcome Variables

Ordinal outcomes

Continuous Outcomes

Failure Time Outcomes

Stratification

2.4 The Surrogate Outcome

2.4.1 Criteria for Surrogacy

2.4.2 Examples

2.4.3 Statistical Surrogate Validation

2.5 Composite Outcomes

2.5.1 Combining Multiple Outcomes

O’Brien’s Rank-sum Procedure

2.5.2 Composite Failure Time Outcomes

Rationale for the use of Composite Failure Time Outcomes

Multiple Nonfatal Event Types

Recommendations Regarding the use of Composite Failure Time Outcomes

2.5.3 Use of Baseline Measurement

2.6 Summary

2.7 Problems

3 Study Design

3.1 Early Phase Trials

3.1.1 Phase I trials

3.1.2 Phase II Trials

3.2 Phase III/IV Trials

3.2.1 Types of Control Groups

Historical Controls

Concurrent Controls

Randomized Control Trials

3.2.2 Common Randomized Control Designs

Parallel Group Design

Randomized Withdrawal Design

Crossover Design

Factorial Designs

Group-randomization Designs

3.3 Non-inferiority Designs

3.4 Screening, Prevention, and Therapeutic Designs

3.5 Adaptive Designs

3.5.1 Sequential Designs

3.5.2 Outcome Based Adaptive Design

3.6 Conclusions

3.7 Problems

4 Sample Size

4.1 Sample Size versus Information

4.2 A General Setup for Frequentist Designs

4.2.1 Two Sample Binomial

4.3 Loss to Follow-up and Non-adherence

4.3.1 Loss to Follow-up

4.3.2 Non-adherence

4.4 Survival Data

4.4.1 Exponential Case

Common Censoring Time

Loss to Follow-up

4.4.2 Time-dependent Rates of Failure, Loss to Follow-up, and Non-adherence

Shoenfeld’s Formula

Markov Chain Model of Lakatos

4.5 Clustered Data

4.6 Tests for Interaction

4.7 Equivalence/Non-inferiority Trials

4.8 Other Considerations

4.9 Problems

5 Randomization

5.1 The Role of Randomization

5.1.1 Confounding

5.1.2 Selection Bias

5.1.3 Population vs. Randomization Models

5.1.4 Statistical Inference

5.2 Fixed Randomization Procedures

5.2.1 Random Allocation Rule

5.2.2 Complete Randomization

5.2.3 Permuted-Block Randomization

5.3 Treatment- and Response-Adaptive Randomization Procedures

5.3.1 Biased Coin Randomization

5.3.2 Urn Randomization

5.3.3 Play-the-Winner Rule

Randomized Play-the- Winner Rule

5.4 Covariate-Adaptive Randomization Procedures

5.4.1 Stratification

5.4.2 Pocock-Simon

5.5 Summary and Recommendations

5.6 Problems

6 Data Collection and Quality Control

6.1 Planning for Collection of Clinical Trial Data

6.1.1 Identification of Information Required

General Issues

Key Categories of Data

External Factors Influencing Data Collection

6.1.2 Mechanisms of Data Collection

IVES

Clinical Case Report Forms (CEFs)

Expedited Safety Reports

Central/Reference Laboratories

Endpoint Tracking and Adjudication

Schedule of Data Collection and Submission

6.1.3 CRF Design and Review

General Principles of Form Design

Organization of the CRF

Header Information

Form Layout

Item Formats and Content

Changes to CRFs During a Trial

Relationship Between the CRF and Database

6.2 Categories of Clinical Data

6.2.1 Subject Identification and Treatment Assignment

6.2.2 Screening and Baseline Information

6.2.3 Follow-up Visits, Tests, and Procedures

6.2.4 Adherence to Study Treatment

6.2.5 Adverse Experiences

6.2.6 Concomitant Medications and Interventions

6.2.7 Clinical Endpoints

6.2.8 Subject Treatment, Follow-up, and Survival Status

6.3 Data Quality Control

6.3.1 QC During Data Collection and Entry

6.3.2 QC by the Data Management Center

Auditing

6.3.3 QC of External Laboratories and Review Committees

6.3.4 QC by the Data Analysis Center

Verification of Treatment Assignments

Examination of Datasets

Treatment of Missing, Erroneous, and Inconsistent Data

Interactions with the Data Management Center

6.4 Conclusions

7 Survival Analysis

7.1 Background

7.2 Estimation of Survival Distributions

7.2.1 Parametric Approach

7.2.2 Nonparametric Approach

7.2.3 The Kaplan-Meier Estimator

7.3 Comparison of Survival Distributions

7.3.1 Parametric Methods

7.3.2 Nonparametric Methods

7.4 Regression Models

7.4.1 Parametric Models

7.4.2 Semiparametric Models

7.4.3 Cox Partial Likelihood

7.4.4 Estimation and Testing

7.4.5 Estimation of the baseline hazard Λ0

7.4.6 Residuals

7.5 Composite Outcomes

7.6 Summary

7.7 Problems

8 Longitudinal Data

8.1 A Clinical Longitudinal Data Example

8.2 The Subject-specific Model

8.3 Two-stage Estimation

8.3.1 Combined Estimation

8.4 The Random-effects, Subject-specific Model

8.4.1 The Model

8.4.2 Estimation for the Random-Effects Model

8.4.3 Example: Ramus Height Data

8.4.4 Marginal Versus Conditional Models

8.4.5 Serial Conditional Correlation

8.5 The Population-average (Marginal) Model

8.5.1 Varying Design Matrices

8.6 Restricted Maximum Likelihood Estimation (REML)

8.7 Standard Errors

8.7.1 Maximum and Restricted Maximum Likelihood

8.7.2 Robust Estimation of Var(β)

8.8 Testing

8.8.1 Nested Models

8.8.2 Non-nested Models

8.8.3 Example: Bone Density

8.9 Additional Levels of Clustering

8.10 Generalized Estimating Equations for Non-normal Data

8.10.1 Examples

8.10.2 The Model for Longitudinal Data

8.10.3 Example: Epilepsy Data

8.11 Missing Data

8.12 Summary

9 Quality of Life

9.1 Defining QoL

9.2 Types of QoL Assessments

9.2.1 Subject Preference Measures

9.2.2 Health Status and Functional Measures

9.3 Selecting a QoL Instrument

9.3.1 Purpose of the Assessment

9.3.2 Validity

9.3.3 Reliability

9.3.4 Sensitivity and Responsiveness

9.3.5 Determining Clinically Meaningful Differences

9.4 Developing a QoL Instrument

9.5 Quality of Life Data

9.5.1 General Issues

9.5.2 Data Collection Considerations

9.6 Analysis of QoL Data

9.6.1 Longitudinal Data Analysis

Graphical Summaries

Summary Measures

Analysis at each Time Separately

Statistical Modeling for Repeated QoL Measurements

9.6.2 Multivariate Analysis

A Global Test Statistic

Latent Variable Models

Quality-Adjusted Survival Analysis

9.7 Summary

10 Data Monitoring and Interim Analysis

10.1 Data and Safety Monitoring

10.2 Examples

10.2.1 Beta-Blocker Heart Attack Trial

10.2.2 Multicenter Automatic Defibrillator Implantation Trial

10.3 The Repeated Testing Problem

10.3.1 Sampling to a Foregone Conclusion

10.3.2 The General Setup

10.3.3 Repeated Significance Tests

10.4 Group Sequential Tests

10.4.1 Classical Group Sequential Methods

10.4.2 Early Stopping in Favor of the Null Hypothesis: One-Sided Tests

10.4.3 Early Termination in BHAT

10.4.4 Alpha Spending Approach

10.5 Triangular Test

10.5.1 Triangular Test for Normal Data

10.5.2 General Form of the Triangular Test for Continuous Monitoring

10.5.3 Triangular Test with Discrete Monitoring

10.5.4 Triangular Test in MADIT Study

10.6 Curtailment Procedures

10.6.1 Deterministic Curtailment

10.6.2 Stochastic Curtailment

10.6.3 B-value and Conditional Power

10.6.4 Predictive Power

10.7 Inference Following Sequential Tests

10.7.1 Observed Significance

10.7.2 Confidence Interval Estimation

10.7.3 Point Estimation

10.7.4 Inference in MADIT

10.8 Discussion

10.8.1 Comparison of boundaries

10.8.2 Perspectives Regarding Interim Monitoring

10.8.3 Discretion in Data Monitoring

10.8.4 Symmetry

10.8.5 Additional Comments

10.9 Problems

11 Selected Issues in the Analysis

11.1 Bias in the Analysis of Clinical Trial Data

11.2 Choice of Analysis Population

11.2.1 The Intent-To-Treat Principle

Objections to ITT

Implementation of ITT

11.2.2 Nonadherence to Assigned Treatment

11.2.3 Ineligibility

Examples

11.2.4 Model-based Alternatives to ITT

Complier Average Causal Effect

Efron and Feldman Causal Model

11.3 Missing Data

11.3.1 Terminology

11.3.2 Sensitivity Analysis

11.3.3 Imputation

Last Observation Carried Forward

11.3.4 Censoring in Survival Analyses

11.3.5 Competing Risks

11.4 Subgroup Analyses

11.4.1 Baseline Subgroups

11.4.2 Subgroups Defined by a Post-Randomization Outcome

11.5 Multiple Testing Procedures

Example

Additional Notation

11.5.1 Bonferroni Procedure

11.5.2 Closed Testing Procedures

11.5.3 Other Multiple Testing Procedures

11.6 Summary

11.7 Problems

12 Closeout and Reporting

12.1 Closing Out a Trial

12.2 Reporting Trial Results

12.2.1 Oral Presentation

12.2.2 Scientific Publication

Introduction

Methods Section

Results Section

Discussion Section

Authorship/Attribution

12.3 Problems

People also search for Introduction to Statistical Methods for Clinical Trials 1st :

introduction to statistical methods for clinical trials pdf

statistical methods for clinical trials

introduction to statistical methods and data analysis

introduction to statistical methods

clinical trials statistical analysis

Tags: Bradley Carlin, Thomas Cook, Julian Faraway, Jim Zidek, Martin Tanner, David DeMets, Statistical Methods, Clinical Trials