# Statistics Applied to Clinical Trials

Springer Science & Business Media, Apr 30, 2016 - Medical - 366 pages
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 Copyright