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E-raamat: Clinical Trial Data Analysis Using R and SAS

, (Georgia Southern University,USA), (University of North Carolina, USA)
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Review of the First Edition

"The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods."Journal of Statistical Software

Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The books practical, detailed approach draws on the authors 30 years experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data.

Whats New in the Second Edition











Adds SAS programs along with the R programs for clinical trial data analysis.





Updates all the statistical analysis with updated R packages.





Includes correlated data analysis with multivariate analysis of variance.





Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials.





Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.

Arvustused

" . . . this book provides a very useful overview of the statistical methods used in the analysis of clinical trials, along with their implementations. This will particularly help clinical practitioners to apply these methodologies in their own scientific problems . . . I would really like to thank the authors, D. Chen, K. E. Peace and P. Zhang, for such a nice readymade reference for clinical trial analysis, with very interesting real data illustrations." ~Abhik Ghosh, International Society for Clinical Biostatistics

List of Figures xix
List of Tables xxi
Preface for the Second Edition xxiii
Preface for the First Edition xxvii
About the Authors xxxi
1 Introduction to R 1(16)
1.1 What is R?
1(1)
1.2 Steps on Installing R and Updating R Packages
2(3)
1.2.1 First Step: Install R Base System
3(1)
1.2.2 Second Step: Installing and Updating R Packages
3(1)
1.2.3 Steps to Get Help and Documentation
4(1)
1.3 R for Clinical Trials
5(2)
1.4 A Simple Simulated Clinical Trial
7(8)
1.4.1 Data Simulation
7(6)
1.4.1.1 R Functions
7(1)
1.4.1.2 Data Generation and Manipulation
8(2)
1.4.1.3 Basic R Graphics
10(3)
1.4.2 Data Analysis
13(2)
1.5 Summary and Recommendations for Further Reading
15(1)
1.6 Appendix: SAS Programs
15(2)
2 Overview of Clinical Trials 17(14)
2.1 Introduction
17(1)
2.2 Phases of Clinical Trials and Objectives
17(3)
2.2.1 Phase 0 Trials
17(1)
2.2.2 Phase I Trials
18(1)
2.2.3 Phase II Trials
18(1)
2.2.4 Phase III Trials
19(1)
2.2.5 Phase IV Trials
19(1)
2.3 The Clinical Development Plan
20(1)
2.4 Biostatistical Aspects of a Protocol
20(9)
2.4.1 Background or Rationale
21(1)
2.4.2 Objective
21(1)
2.4.3 Plan of Study
22(2)
2.4.3.1 Study Population
22(1)
2.4.3.2 Study Design
22(2)
2.4.3.3 Problem Management
24(1)
2.4.4 Statistical Analysis Section
24(3)
2.4.4.1 Study Objectives as Statistical Hypotheses
24(1)
2.4.4.2 Endpoints
25(1)
2.4.4.3 Statistical Methods
25(1)
2.4.4.4 Statistical Monitoring Procedures
26(1)
2.4.5 Statistical Design Considerations
27(1)
2.4.6 Subset Analyses
28(1)
2.5 Concluding Remarks
29(2)
3 Treatment Comparisons in Clinical Trials 31(32)
3.1 Data from Clinical Trials
31(3)
3.1.1 Diastolic Blood Pressure
31(2)
3.1.2 Clinical Trial on Duodenal Ulcer Healing
33(1)
3.2 Statistical Models for Treatment Comparisons
34(5)
3.2.1 Models for Continuous Endpoints
34(4)
3.2.1.1 Student's t-Tests
34(2)
3.2.1.2 One-Way Analysis of Variance (ANO\A)
36(1)
3.2.1.3 Multi-Way ANOVA: Factorial Design
37(1)
3.2.1.4 Multivariate Analysis of Variance (MANOVA)
38(1)
3.2.2 Models for Categorical Endpoints: Pearson's x2-test
38(1)
3.3 Data Analysis in R
39(17)
3.3.1 Analysis of the DBP Trial
39(13)
3.3.1.1 Preliminary Data analysis
39(1)
3.3.1.2 t-test
40(3)
3.3.1.3 Bootstrapping Method
43(1)
3.3.1.4 One-Way ANOVA for Time Changes
44(3)
3.3.1.5 Two-Way ANOVA for Interaction
47(4)
3.3.1.6 MANOVA for Treatment Difference
51(1)
3.3.2 Analysis of Duodenal Ulcer Healing Trial
52(11)
3.3.2.1 Using Pearson's x2-test
52(2)
3.3.2.2 Using Contingency Table
54(2)
3.4 Summary and Conclusions
56(1)
3.5 Appendix: SAS Programs
57(6)
4 Treatment Comparisons in Clinical Trials with Covariates 63(32)
4.1 Data from Clinical Trials
63(2)
4.1.1 Diastolic Blood Pressure
63(1)
4.1.2 Clinical Trials for Betablockers
64(1)
4.1.3 Clinical Trial on Familial Adenomatous Polyposis
64(1)
4.2 Statistical Models Incorporating Covariates
65(8)
4.2.1 ANCOVA Models for Continuous Endpoints
65(3)
4.2.2 Logistic Regression for Binary/Binomial Endpoints
68(2)
4.2.3 Poisson Regression for Clinical Endpoint with Counts
70(1)
4.2.4 Overdispersion
70(3)
4.3 Data Analysis in R
73(17)
4.3.1 Analysis of DBP Trial
73(7)
4.3.1.1 Analysis of Baseline Data
73(3)
4.3.1.2 ANCOVA of DBP Change from Baseline
76(3)
4.3.1.3 MANCOVA for DBP Change from Baseline
79(1)
4.3.2 Analysis of Betablocker Trial
80(6)
4.3.3 Analysis of Data from Familial Adenomatous Polyposis Trial
86(4)
4.4 Summary and Conclusions
90(1)
4.5 Appendix: SAS Programs
91(4)
5 Analysis of Clinical Trials with Time-to-Event Endpoints 95(38)
5.1 Clinical Trials with Time-to-Event Data
96(2)
5.1.1 Phase II Trial of Patients with Stage-2 Breast Carcinoma
96(1)
5.1.2 Breast Cancer Trial with Interval-Censored Data
97(1)
5.2 Statistical Models
98(3)
5.2.1 Primary Functions and Definitions
98(2)
5.2.1.1 The Hazard Function
98(1)
5.2.1.2 The Survival Function
99(1)
5.2.1.3 The Death Density Function
99(1)
5.2.1.4 Relationships between These Functions
100(1)
5.2.2 Parametric Models
100(1)
5.2.2.1 The Exponential Model
100(1)
5.2.2.2 The Weibull Model
100(1)
5.2.2.3 The Rayleigh Model
101(1)
5.2.2.4 The Gompertz Model
101(1)
5.2.2.5 The Lognormal Model
101(1)
5.3 Statistical Methods for Right-Censored Data
101(3)
5.3.1 Nonparametric Models: Kaplan-Meier Estimator
101(1)
5.3.2 Cox Proportion Hazards Regression
102(2)
5.4 Statistical Methods for Interval-Censored Data
104(4)
5.4.1 Turnbull's Nonparametric Estimator
105(2)
5.4.2 Parametric Likelihood Estimation with Covariates
s106
5.4.3 Semiparametric Estimation: the IntCox
107(1)
5.5 Step-by-Step Implementations in R
108(21)
5.5.1 Stage-2 Breast Carcinoma
108(8)
5.5.1.1 Fit Kaplan-Meier
108(4)
5.5.1.2 Fit Weibull Parametric Model
112(2)
5.5.1.3 Fit Cox Regression Model
114(2)
5.5.2 Breast Cancer with Interval-Censored Data
116(17)
5.5.2.1 Fit Turnbull's Nonparametric Estimator
116(6)
5.5.2.2 Fit Turnbull's Nonparametric Estimator Using R Package interval
122(1)
5.5.2.3 Fitting Parametric Models
123(2)
5.5.2.4 Testing Treatment Effect Using Semiparametric Estimation: IntCox
125(3)
5.5.2.5 Testing Treatment Effect Using Semiparametric Estimation: ictest
128(1)
5.6 Summary and Discussions
129(1)
5.7 Appendix: SAS Programs
129(4)
6 Longitudinal Data Analysis for Clinical Trials 133(32)
6.1 Clinical Trials
133(2)
6.1.1 Diastolic Blood Pressure Data
133(1)
6.1.2 Clinical Trial on Duodenal Ulcer Healing
134(1)
6.2 Statistical Models
135(3)
6.2.1 Linear Mixed Models
135(2)
6.2.2 Generalized Linear Mixed Models
137(1)
6.2.3 Generalized Estimating Equation
138(1)
6.3 Longitudinal Data Analysis for Clinical Trials
138(21)
6.3.1 Analysis of Diastolic Blood Pressure Data
138(14)
6.3.1.1 Data Graphics and Response Feature Analysis
139(7)
6.3.1.2 Longitudinal Modeling
146(6)
6.3.2 Analysis of Cimetidine Duodenal Ulcer Trial
152(15)
6.3.2.1 Preliminary Analysis
152(1)
6.3.2.2 Fit Logistic Regression to Binomial Data
152(3)
6.3.2.3 Fit Generalized Linear Mixed Model
155(2)
6.3.2.4 Fit GEE
157(2)
6.4 Summary and Discussion
159(1)
6.5 Appendix: SAS Programs
160(5)
7 Sample Size Determination and Power Calculations in Clinical Trials 165(50)
7.1 Pre-requisites for Sample Size Determination
165(2)
7.2 Comparison of Two Treatment Groups with Continuous Endpoints
167(9)
7.2.1 Fundamentals
167(2)
7.2.2 Basic Formula for Sample Size Calculation
169(1)
7.2.3 R Function power.t. test
170(3)
7.2.4 Unequal Variance: samplesize Package
173(3)
7.3 Two Binomial Proportions
176(10)
7.3.1 R Function power.prop.test
176(3)
7.3.2 R Library: pwr
179(3)
7.3.3 R Function nBinomial in gsDesign library
182(4)
7.4 Time-to-Event Endpoint
186(4)
7.5 Design of Group Sequential Trials
190(7)
7.5.1 Introduction
190(1)
7.5.2 gsDesign
191(6)
7.6 Longitudinal Trials
197(8)
7.6.1 Longitudinal Trial with Continuous Endpoint
197(6)
7.6.1.1 The Model Setting
197(1)
7.6.1.2 Sample Size Calculations
198(1)
7.6.1.3 Power Calculation
199(1)
7.6.1.4 Example and. R. Illustration
199(4)
7.6.2 Longitudinal Binary Endpoint
203(2)
7.6.2.1 Approximate Sample Size Calculation
203(1)
7.6.2.2 Example and R Implementation
204(1)
7.7 Relative Changes and Coefficient of Variation
205(3)
7.7.1 Introduction
205(1)
7.7.2 Sample Size Calculation Formula
205(1)
7.7.3 Example and R Implementation
206(2)
7.8 Concluding Remarks
208(1)
7.9 Appendix: SAS Programs
208(7)
8 Meta-Analysis of Clinical Trials 215(26)
8.1 Data from Clinical Trials
216(1)
8.1.1 Clinical Trials for Betablockers: Binary Data
216(1)
8.1.2 Data for Cochrane Collaboration Logo: Binary Data
216(1)
8.1.3 Clinical Trials on Amlodipine: Continuous Data
217(1)
8.2 Statistical Models for Meta-Analysis
217(6)
8.2.1 Clinical Hypotheses and Effect Size
218(1)
8.2.2 Fixed-Effects Meta-Analysis Model: The Weighted Average
219(2)
8.2.3 Random-Effects Meta-Analysis Model: DerSimonian-Laird
221(2)
8.2.4 Publication Bias
223(1)
8.3 Data Analysis in R
223(13)
8.3.1 Analysis of Betablocker Trials
223(7)
8.3.1.1 Fitting the Fixed-Effects Model
224(3)
8.3.1.2 Fitting the Random-Effects Model
227(3)
8.3.2 Meta-Analysis for Cochrane Collaboration Logo
230(2)
8.3.3 Analysis of Amlodipine Trial Data
232(9)
8.3.3.1 Load the Library and Data
232(1)
8.3.3.2 Fit the Fixed-Effects Model
232(3)
8.3.3.3 Fit the Random-Effects Model
235(1)
8.4 Summary and Conclusions
236(1)
8.5 Appendix: SAS Programs
237(4)
9 Bayesian Methods in Clinical Trials 241(36)
9.1 Bayesian Models
241(8)
9.1.1 Bayes' Theorem
241(2)
9.1.2 Posterior Distributions for Some Standard Distributions
243(2)
9.1.2.1 Normal Distribution with Known Variance
243(1)
9.1.2.2 Normal Distribution with Unknown Variance
244(1)
9.1.2.3 Normal Regression
244(1)
9.1.2.4 Binomial Distribution
245(1)
9.1.2.5 Multinomial Distribution
245(1)
9.1.3 Simulation from the Posterior Distribution
245(4)
9.1.3.1 Direct Simulation
246(1)
9.1.3.2 Importance Sampling
247(1)
9.1.3.3 Gibbs Sampling
247(1)
9.1.3.4 Metropolis-Hastings Algorithm
248(1)
9.2 R Packages in Bayesian Modeling
249(4)
9.2.1 Introduction
249(1)
9.2.2 R Packages using WinBUGS
250(2)
9.2.2.1 R2WinBUGS
250(1)
9.2.2.2 BRugs
251(1)
9.2.2.3 rbugs
251(1)
9.2.2.4 Typical Usage
251(1)
9.2.3 MCMCpack
252(1)
9.3 MCMC Simulations
253(6)
9.3.1 Normal-Normal Model
253(2)
9.3.2 Beta-Binomial Model
255(4)
9.4 Bayesian Data Analysis
259(12)
9.4.1 Blood Pressure Data: Bayesian Linear Regression
259(2)
9.4.2 Binomial Data: Bayesian Logistic Regression
261(5)
9.4.3 Count Data: Bayesian Poisson Regression
266(1)
9.4.4 Comparing Two Treatments
267(4)
9.5 Summary and Discussion
271(1)
9.6 Appendix: SAS Programs
271(6)
10 Bioequivalence Clinical Trials 277(38)
10.1 Data from Bioequivalence Clinical Trials
277(2)
10.1.1 Data from Chow and Liu (2009)
277(1)
10.1.2 Bioequivalence Trial on Cimetidine Tablets
277(2)
10.2 Bioequivalence Clinical Trial Endpoints
279(2)
10.3 Statistical Methods to Analyze Bioequivalence
281(5)
10.3.1 Decision CIs for Bioequivalence
282(1)
10.3.2 The Classical Asymmetric Confidence Interval
283(1)
10.3.3 Westlake's Symmetric Confidence Interval
283(1)
10.3.4 Two One-Sided Tests
284(1)
10.3.5 Bayesian Approaches
284(1)
10.3.6 Individual-Based Bienayme-Tchebycheff (BT) Inequality CI
285(1)
10.3.7 Individual-Based Bootstrap CIs
286(1)
10.4 Step-by-Step Implementation in R
286(28)
10.4.1 Analyze the data from Chow and Liu (2009)
286(13)
10.4.1.1 Load the data into R
286(2)
10.4.1.2 Tests for Carryover Effect
288(2)
10.4.1.3 Test for Direct Formulation Effect
290(2)
10.4.1.4 Analysis of Variance
292(1)
10.4.1.5 Decision CIs
293(1)
10.4.1.6 Classical Shortest 90% CI
293(1)
10.4.1.7 The Westlake CI
294(1)
10.4.1.8 Two One-Sided Tests
295(1)
10.4.1.9 Bayesian Approach
295(1)
10.4.1.10 Individual-Based BT CI
295(1)
10.4.1.11 Bootstrap CIs
296(3)
10.4.2 Analyze the data from Cimetidine Trial
299(18)
10.4.2.1 Clinical Trial Endpoints Calculations
299(5)
10.4.2.2 ANOVA: Tests for Carryover and Other Effects
304(4)
10.4.2.3 Decision CIs
308(1)
10.4.2.4 Classical Shortest 90% CI
308(1)
10.4.2.5 The Westlake CI
309(1)
10.4.2.6 Two One-Sided CIs
309(1)
10.4.2.7 Bayesian Approach
310(1)
10.4.2.8 Individual-Based BT CI
310(1)
10.4.2.9 Bootstrap CIs
311(3)
10.5 Summary and Conclusions
314(1)
10.6 Appendix: SAS Program
314(1)
11 Adverse Events in Clinical Trials 315(22)
11.1 Adverse Event Data from a Clinical Trial
315(2)
11.2 Statistical Methods
317(4)
11.2.1 Confidence Interval (CI) Methods
318(1)
11.2.1.1 Comparison Using Direct CI Method
318(1)
11.2.1.2 Comparison Using Indirect CI Methods
318(1)
11.2.2 Significance Level Methods (SLMs)
319(2)
11.2.2.1 SLM using normal approximation
319(1)
11.2.2.2 SLM using exact binomial distribution
320(1)
11.2.2.3 SLM using resampling from pooled samples
320(1)
11.2.2.4 SLM using resampling from pooled AE rates
321(1)
11.3 Step-by-Step Implementation in R
321(12)
11.3.1 Clinical Trial Data Manipulation
321(1)
11.3.2 R Implementations for CI Methods
322(1)
11.3.3 R Implementations for Indirect CI Methods
323(4)
11.3.4 R for Significant Level Methods
327(10)
11.3.4.1 R for SLM with normal approximation
327(1)
11.3.4.2 R for SLM with exact binomial
328(2)
11.3.4.3 R for SLM using Sampling-Resampling
330(3)
11.4 Summary and Discussions
333(1)
11.5 Appendix: SAS Programs
334(3)
12 Analysis of DNA Microarrays in Clinical Trials 337(26)
12.1 DNA Microarray
337(3)
12.1.1 Introduction
337(1)
12.1.2 DNA, RNA, and Genes
338(1)
12.1.3 Central Dogma of Molecular Biology
338(1)
12.1.4 Probes, Probesets, Mismatch, and Perfectmatch
339(1)
12.1.5 Microarray and Statistical Analysis
340(1)
12.1.6 Software: R/Biocenductor
340(1)
12.2 Breast Cancer Data
340(21)
12.2.1 Data Source
341(2)
12.2.2 Low-Level Data Analysis
343(9)
12.2.2.1 Introduction
343(1)
12.2.2.2 Library affy
344(3)
12.2.2.3 Quality Control
347(2)
12.2.2.4 Background, Normalization, and Summarization
349(3)
12.2.3 High-Level Analysis
352(9)
12.2.3.1 Statistical t-test
354(1)
12.2.3.2 Model Fitting
355(5)
12.2.3.3 Number of Significantly Expressed Genes
360(1)
12.2.4 Functional Analysis of Gene Lists
361(1)
12.3 Concluding Remarks
361(1)
12.4 Appendix: SAS Programs
362(1)
Bibliography 363(10)
Index 373
Ding-Geng (Din) Chen, Ph.D., is a professor at the University of Rochester Medical Center. Dr. Chen has vast experience in

biostatistical research and clinical trial development and methodology. He has authored or co-authored more than 100 journal

publications on biostatistical methodologies and applications. He is also the co-author (with Dr. Peace) of Clinical Trial Methodology

and Clinical Trial Data Analysis Using R and a co-editor (with Drs. Sun and Peace) of Interval-Censored Time-to-Event Data: Methods

and Applications. He is a member of the American Statistical Association, chair for the STAT section of the American Public Health

Association, an associate editor of the Journal of Statistical Computation and Simulation, and an editorial board member of several

other journals.

Karl E. Peace, Ph.D., is the Georgia Cancer Coalition Distinguished Cancer Scholar, senior research scientist, and professor of

biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University. He is also an adjunct professor of

biostatistics at the VCU School of Medicine. Dr. Peace is a reviewer or editor of several journals, the founding editor of the Journal of

Biopharmaceutical Statistics, and a fellow of the American Statistical Association. He has authored or co-authored over 150 articles

and 10 books. He has received numerous awards, including the University System of Georgia Board of Regents Alumni Hall of Fame

Award, the First Presidents Medal for outstanding contributions to Georgia Southern University, and distinguished meritorious service

awards from the American Public Health Association and other organizations. In 2012, the American Statistical Association created the

Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society.
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