Statistics Applied to Clinical Trials

Front Cover
Springer Science & Business Media, Apr 30, 2016 - Medical - 366 pages
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Prior to approval, clinical trial protocols are routinely scrutinized by different circumstantial organs, including ethics committees, institutional and federal review boards, and monitoring committees who conduct interim analyses. This book explains classical statistical analyses of clinical trials and addresses relatively novel issues.
 

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Contents

HYPOTHESES DATA STRATIFICATION 1 General considerations
1
efficacy and safety
2
continuous data
3
proportions percentages and contingency tables
8
correlation coefficient
11
Stratification issues
13
Randomized versus historical controls
14
Factorial designs
15
Purposes of linear regression analysis
137
Another real data example of multiple linear regression exploratory purpose
138
Conclusions
140
CONFOUNDING INTERACTION SYNERGISM 1 Introduction
141
Model
142
I Increased precision of efficacy
144
II Confounding
145
III Interaction and synergism
146

References
16
THE ANALYSIS OF EFFICACY DATA OF DRUG TRIALS 1 Overview
17
The principle of testing statistical significance
18
The TValue standardized mean result of study
21
Unpaired TTest
22
Nullhypothesis testing of 3 or more unpaired samples
24
Three methods to test statistically a paired sample
25
Nullhypothesis testing of 3 or more paired samples
28
Paired data with a negative correlation
30
Rank testing
36
Conclusions
39
THE ANALYIS OF SAFETY DATA OF DRUG TRIALS 1 Introduction summary display
41
Four methods to analyze two unpaired proportions
42
Chisquare to analyze more than two unpaired proportions
48
McNemars test for paired proportions
51
Survival analysis
52
Odds ratio method for analyzing two unpaired proportions
54
Odds ratios for 1 group two treatments
57
CHAPTER 4EQUIVALENCE TESTING 1 Introduction
59
Overview of possibilities with equivalence testing
61
Calculations
62
Equivalence testing a new gold standard?
63
level of correlation in paired equivalence studies
64
Conclusions
65
STATISTICAL POWER AND SAMPLE SIZE 1 What is statistical power
67
Emphasis on statistical power rather than nullhypothesis testing
68
Power computations
70
Example of power computation using the TTable
71
Calculation of required sample size rationale
73
References
78
INTERIM ANALYSES 1 Introduction
79
Interim analysis
80
Groupsequential design of interim analysis
83
Conclusions
85
MULTIPLE STATISTICAL INFERENCES 1 Introduction
87
variables
92
Conclusions
95
CONTROLLING THE RISK OF FALSE POSITIVE CLINICAL TRIALS 1 Introduction
97
Bonferroni test
99
Composite endpoint procedures
100
Conclusions
101
THE INTERPRETATION OF THE PVALUES 1 Introduction
103
Standard interpretation of pvalues
104
Common misunderstandings of the pvalues
106
The real meaning of very large pvalues like p0 95
107
Pvalues larger than 0 95 examples Table 2
108
The real meaning of very small pvalues like p0 0001
109
Pvalues smaller than 0 0001 examples Table 3
110
Discussion
111
Conclusions
113
RESEARCH DATA CLOSER TO EXPECTATION THAN COMPATIBLE WITH RANDOM SAMPLING 1 Introduction
117
Methods and results
118
Discussion
119
Conclusions
122
PRINCIPLES OF LINEAR REGRESSION 1 Introduction
125
More on paired observations
126
Using statistical software for simple linear regression
129
Multiple linear regression
131
Multiple linear regression example
133
Estimation and hypothesis testing
147
Goodnessoffit
148
Selection procedures
149
Discussion
160
Logistic regression
169
Discussion
175
the underlying mechanism 3 Regression model for parallelgroup trials with continuous efficacy data
181
Conclusions
185
Logistic regression equation
190
INTERACTION EFFECTS IN CLINICAL TRIALS 1 Introduction 2 What exactly is interaction a hypothesized example
193
How to test the presence of interaction effects statistically a real data example
196
Additional real data examples of interaction effects
198
Discussion 6 Conclusions
203
References
204
METAANALYSIS 1 Introduction
205
Examples
206
Clearly defined hypotheses Thorough search of trials Strict inclusion criteria
208
Uniform data analysis
209
Discussion where are we now?
217
References
218
1 Introduction
219
Mathematical model
220
Hypothesis testing
221
Statistical power of testing
223
Discussion
226
Conclusion
227
References
228
CROSSOVER STUDIES WITH BINARY RESPONSES 1 Introduction
229
Assessment of carryover and treatment effect
230
Statistical model for testing treatment and carryover effects
231
not have been used 4 Estimate of the size of the problem by review of hypertension
245
Defining QOL in a subjective or objective way
251
Results
252
Discussion
257
Genetics genomics proteonomics data mining
264
RELATIONSHIP AMONG STATISTICAL DISTRIBUTIONS
271
nullhypothesis testing with chisquare distribution 6 Examples of data where variance is more important than mean
278
CLINICAL DATA WHERE VARIABILITY IS MORE
297
Examples
304
2
317
References 28
328
Discussion
335
Conclusions
336
CHAPTER 30STATISTICS IS NO BLOODLESS ALGEBRA 1 Introduction
337
Conclusions
338
Statistics is not like algebra bloodless
339
Statistics can turn art into science
340
Statistics can help the clinician to better understand limitations and benefits of current research
341
Conclusions
342
References
343
BIAS DUE TO CONFLICTS OF INTERESTS SOME GUIDELINES 1 Introduction
345
Need for circumspection recognized
346
Flawed procedures jeopardizing current clinical trials
347
The good news
348
References
350
APPENDIX
353
236
360
Relationship between the normaldistribution and chisquare distribution
364
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