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关键词：Clinical Analysis Analysi alysis clinic

CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2011 by Taylor and Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business
No claim to original U.S. Government works
Printed in the United States of America on acid-free paper
10 9 8 7 6 5 4 3 2 1
International Standard Book Number-13: 978-1-4398-4021-4 (Ebook-PDF)
Preface xvii
List of Figures xxi
List of Tables xxiii
1 Introduction to R 1
1.1 What is R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Steps on Installing R and Updating R Packages . . . . . . . . 2
1.2.1 First Step: Install R Base System . . . . . . . . . . . . 3
1.2.2 Second Step: Installing and Updating R Packages . . . 4
1.2.3 Steps to Get Help and Documentation . . . . . . . . . 4
1.3 R for Clinical Trials . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 A Simple Simulated Clinical Trial . . . . . . . . . . . . . . . 7
1.4.1 Data Simulation . . . . . . . . . . . . . . . . . . . . . 7
1.4.1.1 R Functions . . . . . . . . . . . . . . . . . . . 7
1.4.1.2 Data Generation and Manipulation . . . . . 8
1.4.1.3 Basic R Graphics . . . . . . . . . . . . . . . . 10
1.4.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . 13
1.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 15
2 Overview of Clinical Trials 17
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 Phases of Clinical Trials and Objectives . . . . . . . . . . . . 18
2.2.1 Phase I Trials . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.2 Phase II Trials . . . . . . . . . . . . . . . . . . . . . . 19
2.2.3 Phase III Trials . . . . . . . . . . . . . . . . . . . . . . 19
2.3 The Clinical Development Plan . . . . . . . . . . . . . . . . . 20
2.4 Biostatistical Aspects of a Protocol . . . . . . . . . . . . . . 20
2.4.1 Background or Rationale . . . . . . . . . . . . . . . . 21
2.4.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.3 Plan of Study . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.3.1 Study Population . . . . . . . . . . . . . . . 22
2.4.3.2 Study Design . . . . . . . . . . . . . . . . . . 22
2.4.3.3 Problem Management . . . . . . . . . . . . . 24
2.4.4 Statistical Analysis Section . . . . . . . . . . . . . . . 24
ix
x Contents
2.4.4.1 Study Objectives as Statistical Hypotheses . 24
2.4.4.2 Endpoints . . . . . . . . . . . . . . . . . . . 25
2.4.4.3 Statistical Methods . . . . . . . . . . . . . . 25
2.4.4.4 Statistical Monitoring Procedures . . . . . . 26
2.4.5 Statistical Design Considerations . . . . . . . . . . . . 27
2.4.6 Subset Analyses . . . . . . . . . . . . . . . . . . . . . 28
2.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 29
3

Treatment Comparisons in Clinical Trials 31
3.1 Data from Clinical Trials . . . . . . . . . . . . . . . . . . . . 32
3.1.1 Diastolic Blood Pressure . . . . . . . . . . . . . . . . . 32
3.1.2 Clinical Trial on Duodenal Ulcer Healing . . . . . . . 32
3.2 Statistical Models for Treatment Comparisons . . . . . . . . 35
3.2.1 Models for Continuous Endpoints . . . . . . . . . . . . 35
3.2.1.1 Student's t-tests . . . . . . . . . . . . . . . . 35
3.2.1.2 One-Way Analysis of Variance (ANOVA) . . 37
3.2.1.3 Multi-Way ANOVA: Factorial Design . . . . 38
3.2.2 Models for Categorical Endpoints: Pearson's 2-test . 38
3.3 Data Analysis in R . . . . . . . . . . . . . . . . . . . . . . . 39
3.3.1 Analysis of the DBP Trial . . . . . . . . . . . . . . . . 39
3.3.1.1 Preliminary Data Analysis . . . . . . . . . . 39
3.3.1.2 t-test . . . . . . . . . . . . . . . . . . . . . . 41
3.3.1.3 Bootstrapping Method . . . . . . . . . . . . 43
3.3.1.4 One-Way ANOVA for Time Changes . . . . 45
3.3.1.5 Two-Way ANOVA for Interaction . . . . . . 47
3.3.2 Analysis of Duodenal Ulcer Healing Trial . . . . . . . 52
3.3.2.1 Using Pearson's 2-test . . . . . . . . . . . . 52
3.3.2.2 Using Contingency Tables . . . . . . . . . . . 53
3.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 56
4 Treatment Comparisons in Clinical Trials with Covariates 57
4.1 Data from Clinical Trials . . . . . . . . . . . . . . . . . . . . 58
4.1.1 Diastolic Blood Pressure . . . . . . . . . . . . . . . . . 58
4.1.2 Clinical Trials for Beta-Blockers . . . . . . . . . . . . 58
4.1.3 Clinical Trial on Familial Andenomatous Polyposis . . 59
4.2 Statistical Models Incorporating Covariates . . . . . . . . . . 60
4.2.1 ANCOVA Models for Continuous Endpoints . . . . . . 60
4.2.2 Logistic Regression for Binary/Binomial Endpoints . . 62
4.2.3 Poisson Regression for Clinical Endpoint with Counts 64
4.2.4 Over-Dispersion . . . . . . . . . . . . . . . . . . . . . 64
4.3 Data Analysis in R . . . . . . . . . . . . . . . . . . . . . . . 67
4.3.1 Analysis of DBP Trial . . . . . . . . . . . . . . . . . . 67
4.3.1.1 Analysis of Baseline Data . . . . . . . . . . . 67
4.3.1.2 Analysis of DBP Change from Baseline with
ANCOVA . . . . . . . . . . . . . . . . . . . 70
Contents xi
4.3.2 Analysis of Beta-Blocker Trial . . . . . . . . . . . . . . 74
4.3.3 Analysis of Data from Familial Andenomatous Polyposis
Trial . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 84
5 Analysis of Clinical Trials with Time-to-Event Endpoints 85
5.1 Clinical Trials with Time-to-Event Data . . . . . . . . . . . . 87
5.1.1 Phase II Trial of Patients with Stage-2 Breast Carcinoma
. . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.1.2 Breast Cancer Trial with Interval-Censored Data . . . 88
5.2 Statistical Models . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2.1 Primary Functions and Denitions . . . . . . . . . . . 88
5.2.1.1 The Hazard Function . . . . . . . . . . . . . 90
5.2.1.2 The Survival Function . . . . . . . . . . . . . 90
5.2.1.3 The Death Density Function . . . . . . . . . 90
5.2.1.4 Relationships among These Functions . . . . 91
5.2.2 Parametric Models . . . . . . . . . . . . . . . . . . . . 91
5.2.2.1 The Exponential Model . . . . . . . . . . . . 92
5.2.2.2 The Weibull Model . . . . . . . . . . . . . . 92
5.2.2.3 The Rayleigh Model . . . . . . . . . . . . . . 92
5.2.2.4 The Gompertz Model . . . . . . . . . . . . . 93
5.2.2.5 The Lognormal Model . . . . . . . . . . . . . 93
5.3 Statistical Methods for Right-Censored Data . . . . . . . . . 93
5.3.1 Nonparametric Models: Kaplan{Meier Estimator . . . 93
5.3.2 Cox Proportional Hazards Regression . . . . . . . . . 94
5.4 Statistical Methods for Interval-Censored Data . . . . . . . . 96
5.4.1 Turnbull's Nonparametric Estimator . . . . . . . . . . 96
5.4.2 Parametric Likelihood Estimation with Covariates . . 97
5.4.3 Semiparametric Estimation: The IntCox . . . . . . . . 98
5.5 Step-by-Step Implementations in R . . . . . . . . . . . . . . 99
5.5.1 Stage-2 Breast Carcinoma . . . . . . . . . . . . . . . . 99
5.5.1.1 Fit Kaplan{Meier . . . . . . . . . . . . . . . 100
5.5.1.2 Fit Weibull Parametric Model . . . . . . . . 104
5.5.1.3 Fit Cox Regression Model . . . . . . . . . . . 107
5.5.2 Breast Cancer Cosmesis Interval-Censored Data . . . 108
5.5.2.1 Fit Turnbull's Nonparametric Estimator . . . 109
5.5.2.2 Fitting Parametric Models . . . . . . . . . . 114
5.5.3 Fitting the Semiparametric Estimation: IntCox . . . . 116
5.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 120
6 Analysis of Data from Longitudinal Clinical Trials 121
6.1 Clinical Trials . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.1.1 Diastolic Blood Pressure Data . . . . . . . . . . . . . 122
6.1.2 Clinical Trial on Duodenal Ulcer Healing . . . . . . . 122
6.2 Statistical Models . . . . . . . . . . . . . . . . . . . . . . . . 123
xii Contents
6.2.1 Linear Mixed Models . . . . . . . . . . . . . . . . . . . 123
6.2.2 Generalized Linear Mixed Models . . . . . . . . . . . . 125
6.2.3 Generalized Estimating Equation . . . . . . . . . . . . 126
6.3 Analysis of Data from Longitudinal Clinical Trials . . . . . . 126
6.3.1 Analysis of Diastolic Blood Pressure Data . . . . . . . 126
6.3.1.1 Preliminary Data Analysis . . . . . . . . . . 127
6.3.1.2 Longitudinal Modeling . . . . . . . . . . . . 135
6.3.2 Analysis of Cimetidine Duodenal Ulcer Trial . . . . . 141
6.3.2.1 Preliminary Analysis . . . . . . . . . . . . . 141
6.3.2.2 Fit Logistic Regression to Binomial Data . . 142
6.3.2.3 Fit Generalized Linear Mixed Model . . . . . 145
6.3.2.4 Fit GEE . . . . . . . . . . . . . . . . . . . . 147
6.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 149
7 Sample Size Determination and Power Calculation in Clinical
Trials 151
7.1 Prerequisites for Sample Size Determination . . . . . . . . . 152
7.2 Comparison of Two Treatment Groups with Continuous Endpoints
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
7.2.1 Fundamentals . . . . . . . . . . . . . . . . . . . . . . . 154
7.2.2 Basic Formula for Sample Size Calculation . . . . . . 156
7.2.3 R Function power.t.test . . . . . . . . . . . . . . . . 157
7.2.4 Unequal Variance: samplesize Package . . . . . . . . 160
7.3 Two Binomial Proportions . . . . . . . . . . . . . . . . . . . 163
7.3.1 R Function power.prop.test . . . . . . . . . . . . . . 163
7.3.2 R Library: pwr . . . . . . . . . . . . . . . . . . . . . . 166
7.3.3 R Function nBinomial in gsDesign Library . . . . . . 168
7.4 Time-to-Event Endpoint . . . . . . . . . . . . . . . . . . . . 173
7.5 Design of Group Sequential Trials . . . . . . . . . . . . . . . 177
7.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 177
7.5.2 gsDesign . . . . . . . . . . . . . . . . . . . . . . . . . 178
7.6 Longitudinal Trials . . . . . . . . . . . . . . . . . . . . . . . 184
7.6.1 Longitudinal Trial with Continuous Endpoint . . . . . 184
7.6.1.1 The Model Setting . . . . . . . . . . . . . . . 184
7.6.1.2 Sample Size Calculations . . . . . . . . . . . 185
7.6.1.3 Power Calculation . . . . . . . . . . . . . . . 186
7.6.1.4 Example and R Illustration . . . . . . . . . . 186
7.6.2 Longitudinal Binary Endpoint . . . . . . . . . . . . . 190
7.6.2.1 Approximate Sample Size Calculation . . . . 190
7.6.2.2 Example and R Implementation . . . . . . . 191
7.7 Relative Changes and Coecient of Variation: An Extra . . 192
7.7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 192
7.7.2 Sample Size Calculation Formula . . . . . . . . . . . . 192
7.7.3 Example and R Implementation . . . . . . . . . . . . . 193
7.8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 195
Contents xiii
8 Meta-Analysis of Clinical Trials 197
8.1 Data from Clinical Trials . . . . . . . . . . . . . . . . . . . . 198
8.1.1 Clinical Trials for Beta-Blockers: Binary Data . . . . . 198
8.1.2 Data from Cochrane Collaboration Logo: Binary Data 199
8.1.3 Clinical Trials on Amlodipine: Continuous Data . . . . 199
8.2 Statistical Models for Meta-Analysis . . . . . . . . . . . . . . 200
8.2.1 Clinical Hypotheses and Eect Size . . . . . . . . . . . 200
8.2.2 Fixed-Eects Meta-Analysis Model: The Weighted-
Average . . . . . . . . . . . . . . . . . . . . . . . . . . 202
8.2.3 Random-Eects Meta-Analysis Model: DerSimonian{
Laird . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
8.2.4 Publication Bias . . . . . . . . . . . . . . . . . . . . . 205
8.3 Meta-Analysis of Data in R . . . . . . . . . . . . . . . . . . . 206
8.3.1 Analysis of Beta-Blocker Trials . . . . . . . . . . . . . 206
8.3.1.1 Fitting the Fixed-Eects Model . . . . . . . 206
8.3.1.2 Fitting the Random-Eects Model . . . . . . 209
8.3.2 Analysis of the Cochrane Collaboration Logo Trials . 213
8.3.3 Analysis of Amlodipine Trials Data . . . . . . . . . . . 215
8.3.3.1 Load the Library and Data . . . . . . . . . . 215
8.3.3.2 Fit the Fixed-Eects Model . . . . . . . . . . 216
8.3.3.3 Fit the Random-Eects Model . . . . . . . . 219
8.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 220

9 Bayesian Analysis Methods in Clinical Trials 223
9.1 Bayesian Models . . . . . . . . . . . . . . . . . . . . . . . . . 224
9.1.1 Bayes' Theorem . . . . . . . . . . . . . . . . . . . . . 224
9.1.2 Posterior Distributions for Some Standard Distributions 226
9.1.2.1 Normal Distribution with Known Variance . 226
9.1.2.2 Normal Distribution with Unknown Variance 227
9.1.2.3 Normal Regression . . . . . . . . . . . . . . . 227
9.1.2.4 Binomial Distribution . . . . . . . . . . . . . 228
9.1.2.5 Multinomial Distribution . . . . . . . . . . . 228
9.1.3 Simulation from the Posterior Distribution . . . . . . 228
9.1.3.1 Direct Simulation . . . . . . . . . . . . . . . 229
9.1.3.2 Importance Sampling . . . . . . . . . . . . . 229
9.1.3.3 Gibbs Sampling . . . . . . . . . . . . . . . . 230
9.1.3.4 Metropolis{Hastings Algorithm . . . . . . . . 231
9.2 R Packages in Bayesian Modeling . . . . . . . . . . . . . . . 232
9.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 232
9.2.2 R Packages Using WinBUGS . . . . . . . . . . . . . . . 233
9.2.2.1 R2WinBUGS . . . . . . . . . . . . . . . . . . . 233
9.2.2.2 BRugs . . . . . . . . . . . . . . . . . . . . . . 234
9.2.2.3 rbugs . . . . . . . . . . . . . . . . . . . . . . 234
9.2.2.4 Typical Usage . . . . . . . . . . . . . . . . . 234
9.2.3 MCMCpack . . . . . . . . . . . . . . . . . . . . . . . . . 235
xiv Contents
9.3 MCMC Simulations . . . . . . . . . . . . . . . . . . . . . . . 236
9.3.1 Normal-Normal Model . . . . . . . . . . . . . . . . . . 236
9.3.2 Beta-Binomial Model . . . . . . . . . . . . . . . . . . 238
9.4 Bayesian Data Analysis . . . . . . . . . . . . . . . . . . . . . 242
9.4.1 Blood Pressure Data: Bayesian Linear Regression . . . 243
9.4.2 Binomial Data: Bayesian Logistic Regression . . . . . 246
9.4.3 Count Data: Bayesian Poisson Regression . . . . . . . 250
9.4.4 Comparing Two Treatments . . . . . . . . . . . . . . . 251
9.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 256
10 Analysis of Bioequivalence Clinical Trials 257
10.1 Data from Bioequivalence Clinical Trials . . . . . . . . . . . 258
10.1.1 Data from Chow and Liu (2009) . . . . . . . . . . . . 258
10.1.2 Bioequivalence Trial on Cimetidine Tablets . . . . . . 258
10.2 Bioequivalence Clinical Trial Endpoints . . . . . . . . . . . . 260
10.3 Statistical Methods to Analyze Bioequivalence . . . . . . . . 262
10.3.1 Decision CIs for Bioequivalence . . . . . . . . . . . . . 262
10.3.2 The Classical Asymmetric Condence Interval . . . . 263
10.3.3 Westlake's Symmetric Condence Interval . . . . . . . 264
10.3.4 Two One-Sided Tests . . . . . . . . . . . . . . . . . . 264
10.3.5 Bayesian Approaches . . . . . . . . . . . . . . . . . . . 265
10.3.6 Individual-Based Bienayme-Tchebyche (BT) Inequality
CI . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
10.3.7 Individual-Based Bootstrap CIs . . . . . . . . . . . . . 267
10.4 Step-by-Step Implementation in R . . . . . . . . . . . . . . . 267
10.4.1 Analyze the Data from Chow and Liu (2009) . . . . . 267
10.4.1.1 Load the Data into R . . . . . . . . . . . . . 267
10.4.1.2 Tests for Carryover Eect . . . . . . . . . . . 269
10.4.1.3 Test for Direct Formulation Eect . . . . . . 271
10.4.1.4 Analysis of Variance . . . . . . . . . . . . . . 273
10.4.1.5 Decision CIs . . . . . . . . . . . . . . . . . . 274
10.4.1.6 Classical Shortest 90% CI . . . . . . . . . . . 274
10.4.1.7 The Westlake Symmetrical CI . . . . . . . . 275
10.4.1.8 Two One-Sided Tests . . . . . . . . . . . . . 276
10.4.1.9 Bayesian Approach . . . . . . . . . . . . . . 276
10.4.1.10 Individual-Based BT CI . . . . . . . . . . . . 276
10.4.1.11 Bootstrap CIs . . . . . . . . . . . . . . . . . 277
10.4.2 Analyze the Data from Cimetidine Trial . . . . . . . . 282
10.4.2.1 Bioavailability Endpoints Calculations . . . . 282
10.4.2.2 ANOVA: Tests for Carryover and Other Effects
. . . . . . . . . . . . . . . . . . . . . . . 286
10.4.2.3 Decision CIs . . . . . . . . . . . . . . . . . . 290
10.4.2.4 Classical Shortest 90% CI . . . . . . . . . . . 290
10.4.2.5 The Westlake Symmetrical CI . . . . . . . . 291
10.4.2.6 Two One-Sided CI . . . . . . . . . . . . . . . 292
Contents xv
10.4.2.7 Bayesian Approach . . . . . . . . . . . . . . 292
10.4.2.8 Individual-Based BT CI . . . . . . . . . . . . 292
10.4.2.9 Bootstrap CIs . . . . . . . . . . . . . . . . . 293
10.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 297
11 Analysis of Adverse Events in Clinical Trials 299
11.1 Adverse Event Data from a Clinical Trial . . . . . . . . . . . 300
11.2 Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . 302
11.2.1 Condence Interval (CI) Methods . . . . . . . . . . . 302
11.2.1.1 Comparison Using Direct CI Method . . . . 302
11.2.1.2 Comparison Using Indirect CI Methods . . . 303
11.2.1.3 Connection between Direct and Indirect CI
Methods . . . . . . . . . . . . . . . . . . . . 304
11.2.2 Signicance Level Methods (SLM) . . . . . . . . . . . 304
11.2.2.1 SLM Using Normal Approximation . . . . . 304
11.2.2.2 SLM Using Exact Binomial Distribution . . 305
11.2.2.3 SLM Using Resampling from Pooled Samples 305
11.2.2.4 SLM Using Resampling from Pooled AE Rates 306
11.3 Step-by-Step Implementation in R . . . . . . . . . . . . . . . 306
11.3.1 Clinical Trial Data Manipulation . . . . . . . . . . . . 306
11.3.2 R Implementations for CI Methods . . . . . . . . . . . 307
11.3.3 R Implementations for Indirect CI Methods . . . . . . 308
11.3.4 R for Signicant Level Methods . . . . . . . . . . . . . 313
11.3.4.1 R for SLM with Normal Approximation . . . 313
11.3.4.2 R for SLM with Exact Binomial . . . . . . . 314
11.3.4.3 R for SLM Using Sampling{Resampling . . . 316
11.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 320
12 Analysis of DNA Microarrays in Clinical Trials 321
12.1 DNA Microarray . . . . . . . . . . . . . . . . . . . . . . . . . 322
12.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 322
12.1.2 DNA, RNA, and Genes . . . . . . . . . . . . . . . . . 322
12.1.3 Central Dogma of Molecular Biology . . . . . . . . . . 323
12.1.4 Probes, Probesets, Mismatch, and Perfect Match . . . 324
12.1.5 Microarray and Statistical Analysis . . . . . . . . . . . 324
12.1.6 Software: R/Bioconductor . . . . . . . . . . . . . . . 324
12.2 Breast Cancer Data . . . . . . . . . . . . . . . . . . . . . . . 325
12.2.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . 326
12.2.2 Low-Level Data Analysis . . . . . . . . . . . . . . . . 328
12.2.2.1 Introduction . . . . . . . . . . . . . . . . . . 328
12.2.2.2 Library affy . . . . . . . . . . . . . . . . . . 329
12.2.2.3 Quality Control . . . . . . . . . . . . . . . . 331
12.2.2.4 Background, Normalization, and Summarization
. . . . . . . . . . . . . . . . . . . . . . . 334
12.2.3 High-Level Analysis . . . . . . . . . . . . . . . . . . . 337
xvi Contents
12.2.3.1 Statistical t-test . . . . . . . . . . . . . . . . 339
12.2.3.2 Model Fitting . . . . . . . . . . . . . . . . . 340
12.2.3.3 Number of Signicantly Expressed Genes . . 345
12.2.4 Functional Analysis of Gene Lists . . . . . . . . . . . . 345
12.3 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 346
Bibliography 349
Index 3

用于医药行业？

太贵了。。。30.。。

太贵了...但是很有用

此书不好找，费了很大的劲，看看各章节标题就知道内容会很棒的~

太贵了，真实的

买来看看，有点贵