Introduction to Randomized Controlled Clinical Trials, Second Edition

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ISBN: 9781584886242 | 1584886242
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Copyright: 6/26/2006

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Evidence from randomized controlled clinical trials is widely accepted as the only sound basis for assessing the efficacy of new medical treatments. Statistical methods play a key role in all stages of these trials, including their justification, design, and analysis. This second edition of Introduction to Randomized Controlled Clinical Trials provides a concise presentation of the principles applied in this area. It details the concepts behind randomization and methods for designing and analyzing trials and also includes information on meta-analysis and specialized designs, such as cross-over trials, cluster-randomized designs, and equivalence studies.This latest edition features new and revised references, examples, exercises, and a new chapter dedicated to binary outcomes and survival analysis. It also presents numerous examples taken from the medical literature, contains exercises at the end of each chapter, and offers solutions in an appendix. The author uses Minitab and R software throughout the text for implementing the methods that are presented.Comprehensive and accessible, Introduction to Randomized Controlled Clinical Trials is well-suited for those familiar with elementary statistical ideas and methods who want to further their knowledge of the subject.

1 What Is a Randomized Controlled Trial?

(14)

1.1 Definition and Key Features

(1)

1.2 Historical Context and the Nature of RCTs

(3)

1.2.1 Historical Background

(1)

1.2.2 Impact of RCTs and Importance of Statistics

(1)

1.2.3 Ethical Issues

(1)

1.3 Structure and Justification of RCTs

(7)

1.3.1 Eligible Patients

(3)

1.3.2 The Need for Concurrent Controls

(2)

1.3.3 Allocation at Random

(6)

1.3.3.1 Comparable Groups

(1)

1.3.3.2 Allocation Unknown at Entry to the RCT

(1)

1.3.3.3 Valid Estimate of Error

(1)

Exercises

(3)

2 Bias

(10)

2.1 What Is Meant by Bias in RCTs?

(1)

2.2 Types of Bias

(6)

2.2.1 Selection Bias

(3)

2.2.2 Allocation Bias

(1)

2.2.3 Assessment Bias

(1)

2.2.4 Publication Bias

(1)

2.2.5 Stopping Rules

(1)

Exercises

(3)

3 How Many Patients Do I Need?

(18)

3.1 Criteria for Sample Size Calculations

(2)

3.1.1 The Primary Question

(1)

3.1.2 Adequate Evidence

(1)

3.2 Hypothesis Tests

(3)

3.2.1 General Remarks

(1)

3.2.2 Implication for RCTs

(1)

3.2.3 One-Sided vs. Two-Sided Tests

(1)

3.3 Sample Size for a Normally Distributed Variable

(4)

3.4 Sample Size for a Binary Variable

(4)

3.4.1 Formulation

(1)

3.4.2 The Delta Method

(1)

3.4.3 Sample Size Formula

(2)

3.5 General Remarks about Sample Size Calculations

(1)

Exercises

(4)

4 Methods of Allocation

(22)

4.1 Simple Randomization

(3)

4.1.1 Chance Imbalance of Group Sizes

(2)

4.1.2 Distribution of

(1)

4.1.3 Practical Consequences

(1)

4.2 Random Permuted Blocks

(3)

4.2.1 Random Permuted Blocks of Fixed Length

(1)

4.2.2 RPBs with Random Block Length

(2)

4.3 Biased Coin Designs and Urn Schemes

(4)

4.3.1 Biased Coin Designs

(1)

4.3.2 Urn Models

(3)

4.4 Unequal Randomization

(1)

4.5 Stratification

(4)

4.5.1 The Problem of Allocation Bias

(3)

4.5.2 Using Stratification

(1)

4.6 Minimization

(3)

4.6.1 Minimization for Two-Treatment Trials

(3)

Exercises

(4)

5 Assessment, Blinding, and Placebos

(8)

5.1 Double and Single Blindness

(2)

5.1.1 General Principles

(2)

5.2 Placebos

(4)

5.2.1 The Placebo Effect

(2)

5 2.2 The Role of Placebos Blinding

(2)

5.3 Practical Considerations

Exercises

(2)

6 Analysis Dif Results

(22)

6.1 Example

6.2 Use of Confidence Intervals

(6)

6.3 Baselines: Uses and Abuses

6.3.1 Comparison of Baseline Values

(1)

6.3.2. Analyzing Change from Baseline

(2)

6.3.3 An Erroneous Analysis Based on Change from Baseline

(1)

6.4 Analysis of Covariance

(11)

6.4.1 JBaseline Bias Leads to Outcome Bias

(1)

6.4.2 Interlude: Bivariate Normal Variables

(2)

6.4.3 Allowing for Baseline Imbalance: The Theory

(2)

6.4.4 Allowing for Baseline Imbalance: The Practice

(3)

6.4.5 Conditional vs. Unconditional Inference

(1)

6.4.6 Some General Remarks on Covariates

(7)

6.4.6.1 Choice of Analysis

(1)

6.4.6.2 Other Types of Covariate

(1)

6.4.6.3 Which Covariates Should Be Used?

(2)

Exercises

(4)

7 Further Analysis: Binary and Survival Data

(54)

7.1 Binary Data: An Example and a Statistical Model

(1)

7.2 Point Estimates and Hypothesis Tests

(5)

7.2.1 Methods Based on Informal Derivations

(3)

7.2.2 Methods Based on More Formal Derivations

(2)

7.3 Interval Estimates for the Binary Case

101

(9)

7.3.1 Different Measures of Difference between Treatments

101

(1)

7.3.2 Interval Estimates or Confidence Intervals

102

(8)

7.3.2.1 Interval Estimates for ARD and NNT

102

(2)

7.3.2.2 Problems with the Confidence Interval for ARD and a Simple Solution

104

(3)

7.3.2.3 Interval Estimates for RR and OR

107

(2)

7.3.2.4 Summary of Results

109

(1)

7.4 Adjusting Binary Outcomes for Baseline Observations

110

(13)

7.4.1 Using a Logistic Model

110

(2)

7.4.2 Fitting a Logistic Regression

112

(2)

7.4.3 Adjusting the OR in the PUVA vs. TL-01 Trial

114

(3)

7.4.4 An Alternative Approach

117

(6)

7.4.4.1 The Hypergeometric Distribution

117

(2)

7.4.4.2 The Mantel—Haenszel Method

119

(4)

7.5 Survival Analysis

123

(15)

7.5.1 The Hazard Function, the Survival Curve, and Some Estimators

124

(6)

7.5.1.1 The Kaplan—Meier Estimator

126

(1)

7.5.1.2 Parametric Estimators

126

(4)

7.5.2 Comparing Survival Curves

130

(3)

7.5.3 Adjusting Survival Analyses for Baseline Values

133

(5)

7.6 Analyses Using Randomization Models

138

(9)

7.6.1 Randomization Models: Simple Randomization

138

(4)

7.6.2 Randomization Models: Stratified Randomization

142

(9)

7.6.2.1 Stratified Randomization: Exact Analysis

146

(1)

7.6.2.2 Blocked Allocations

147

(1)

Exercises

147

(2)

8 Monitoring Accumulating Data

149

(16)

8.1 Motivation and Problems with Repeated Analysis of Data

149

(2)

8.2 Sequential and Group Sequential Methods

151

(7)

8.2.1 Using Repeated Significance Tests

151

(2)

8.2.2 Some Theory for Group Sequential Designs with Normal Outcomes

153

(2)

8.2.3 Other Forms of Stopping Rule

155

(3)

8.3 Other Approaches to Accumulating Data

158

(4)

8.3.1 SPRT and Triangular Test

158

(2)

8.3.2 Bayesian and Likelihood Approaches

160

(1)

8.3.3 Adaptive Allocation

161

(1)

8.4 Data Monitoring Committees

162

(2)

Exercises

164

(1)

9 Subgroups and Multiple Outcomes

165

(18)

9.1 The Role of Subgroups in Randomized Clinical Trials

165

(1)

9.2 Methods for Comparing Subgroups

166

(4)

9.2.1 Approximate Theory

166

(1)

9.2.2 Practice: Continuous Outcomes

167

(2)

9.2.2.1 An Erroneous Analysis

168

(1)

9.2.3 Practice: Binary Outcomes

169

(1)

9.3 Methods of Selecting Subgroups

170

(2)

9.4 Qualitative Interactions

172

(2)

9.5 Multiple Outcomes

174

(2)

9.6 Correction of P-Values

176

(1)

9.6.1 Boole's Inequality

176

(1)

9.6.2 Application to RCTs

176

(1)

9.7 Some Alternative Methods for Multiple Outcomes

177

(3)

9.7.1 Extension to Bonferroni Inequality

178

(1)

9.7.2 Multivariate Methods

178

(2)

9.7.3 O'Brien's Rank-Based Method

180

(1)

Exercises

180

(3)

10 Protocols and Protocol Deviations

183

(10)

10.1 Protocols: Their Nature and Role

183

(1)

10.2 Protocol Deviation

184

(4)

10.2.1 Ineligible Patients

185

(1)

10.2.2 Administration of Treatment Other than That Allocated

186

(2)

10.3 Analysis by Intention-to-Treat

188

(3)

10.3.1 Informal Description

188

(1)

10.3.2 Theoretical Description

189

(2)

Exercises

191

(2)

11 Some Special Designs: Crossovers, Equivalence, and Clusters

193

(18)

11.1 Crossover Trials

193

(1)

11.2 The AB/BA Design

194

(1)

11.3 Analysis of AB/BA Design for Continuous Outcomes

194

(3)

11.3.1 The Theory

194

(2)

11.3.2 An Application

196

(1)

11.4 The Issue of Carryover

197

(2)

11.5 Equivalence Trials

199

(3)

11.5.1 General Remarks

199

(1)

11.5.2 Sample Sizes for Equivalence Trials with Normally Distributed Outcomes

200

(2)

11.5.3 Comparison of Conventional and Equivalence Trials

202

(1)

11.6 Cluster Randomized Trials

202

(6)

11.6.1 Introduction and Rationale

202

(3)

11.6.2 Methods of Analysis for Cluster-Randomized Trials

205

(1)

11.6.3 Sample Size Estimation for Continuous Outcomes from Cluster-Randomized Trials

206

(1)

11.6.4 General Remarks about Cluster-Randomized Trials

207

(1)

Exercises

208

(3)

12 Meta-Analyses of Clinical Trials

211

(12)

12.1 What Are Meta-Analyses, and Why Are They Needed?

211

(2)

12.2 Some Methodology for Meta-Analysis

213

(4)

12.2.1 Data Collection

213

(1)

12.2.2 Estimating the Overall Effect Using a Fixed-Effects Model

213

(2)

12.2.3 Estimating the Overall Effect Using a Random-Effects Model

215

(2)

12.3 Some Graphical Methods for Meta-Analysis

217

(4)

12.3.1 Meta-Analysis Diagrams

217

(1)

12.3.2 Sensitivity Plots for Random-Effects Analysis

218

(1)

12.3.3 Funnel or Radial Plots

219

(2)

12.4 Some General Issues in Meta-Analysis

221

(1)

12.4.1 Random-Effects and Intertrial Variation

221

(1)

12.4.2 Position of Meta-Analysis

221

(1)

Exercises

222

(1)

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Introduction to Randomized Controlled Clinical Trials, Second Edition

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Introduction to Randomized Controlled Clinical Trials, Second Edition

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