Statistics Applied to Clinical Trials

Naslovnica
Springer Science & Business Media, 27. lip 2011. - Broj stranica: 210
In 1948 the first randomized controlled trial was published by the English Medical Research Council in the British Medical Journal. Until then, observations had been uncontrolled. Initially, trials frequently did not confirm hypotheses to be tested. This phenomenon was attributed to low sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were recognized and subsequently were better accounted for: carryover effects due to insufficient washout from previous treatments, time effects due to external factors and the natural history of the condition under study, bias due to asymmetry between treatment groups, lack of sensitivity due to a negative correlation between treatment responses, etc. Such flaws, mainly of a technical nature, have been largely corrected and led to trials after 1970 being of significantly better quality than before. The past decade has focused, in addition to technical aspects, on the need for circumspection in planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial bodies, including ethics committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses.
This book not only explains classical statistical analyses of clinical trials, but addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, and meta-analyses, and provides a framework of the best statistical methods currently available for such purposes. The book is not only useful for investigators involved in the field of clinical trials, but also for all physicians who wish to better understand the data of trials as currently published.
 

Sadržaj

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
References
16
Goodnessoffit
102
Selection procedures
103
References
104
CURVILINEAR REGRESSION 1 Summary
106
Results
108
Discussion
115
References
116
METAANALYSIS 1 Introduction
120

THE ANALYSIS OF EFFICACY DATA OF DRUG TRIALS 1 Overview
17
The principle of testing statistical significance
18
Unpaired TTest
21
Null hypothesis testing of 3 or more unpaired samples
23
Three methods to test statistically a paired sample
24
Nullhypothesis testing of 3 or more paired samples
28
Paired data with a negative correlation
29
Rank testing
35
References
38
THE ANALYSIS OF SAFETY DATA OF DRUG TRIALS 1 Introduction summary display
39
Four methods to analyze two unpaired proportions
40
Chisquare to analyze more than two unpaired proportions
42
McNemars test for paired proportions
43
Survival analysis
44
Conclusions
46
EQUIVALENCE TESTING
48
Introduction
49
Equivalence testing a new gold standard?
51
STATISTICAL POWER AND SAMPLE SIZE What is statistical power
53
Emphasis on statistical power rather than nullhypothesis testing
54
Power computations
56
Example of power computation using the TTable
57
Calculation of required sample size rationale
59
Testing not only superiority but also inferiority of a new treatment type III
62
References
64
CHAPTER 6INTERIM ANALYSES 1 Introduction
66
Groupsequential design of interim analysis
69
Conclusions
71
MULTIPLE STATISTICAL INFERENCES 1 Introduction 73
75
Primary and secondary variables
78
Conclusions
81
PRINCIPLES OF LINEAR REGRESSION 1 Introduction
83
More on paired observations
84
Using statistical software for simple linear regression
87
Multiple linear regression
89
Another real data example of multiple linear regression
93
Conclusions
94
CONFOUNDING INTERACTION SYNERGISM 1 Introduction
95
Model
96
I Increased precision of efficacy
98
II Confounding
99
III Interaction and synergism
100
Estimation and hypothesis testing
101
Clearly defined hypotheses
121
Strict inclusion criteria
122
Discussion where are we now?
131
References
132
POWER ANALYSIS 1 Summary
133
Mathematical model
134
Hypothesis testing
135
Statistical power of testing
137
Conclusions
140
References
141
CROSSOVER STUDIES WITH BINARY RESPONSES 1 Summary
143
Assessment of carryover and treatment effect
144
Statistical model for testing treatment and carryover effects
145
Results
146
Examples
148
Discussion
149
References
150
Examples
151
Logistic regression equation
154
Conclusions
155
CHAPTER 15QUALITYOFLIFE ASSESSMENTS IN CLINICAL TRIALS 1 Summary 157
158
Defining QOL in a subjective or objective way
160
Lack of sensitivity of QOLassessments
162
Discussion
165
References
166
STATISTICS FOR THE ANALYSIS OF GENETIC DATA 1 Introduction
167
Some terminology
168
Genetics genomics proteonomics data mining
170
Genomics
171
Conclusions
175
RELATIONSHIP AMONG STATISTICAL DISTRIBUTIONS 1 Summary
177
Variances
178
The normal distribution
179
Nullhypothesis testing with the normal or the tdistribution
181
Relationship between the normal distribution and chisquare distribution
183
Examples of data where variance is more important than mean
185
Chisquare can be used for multiple samples of data
186
Conclusions
188
References
189
Statistical principles can help to improve the quality of the trial
192
INDEX
207
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