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The authors have both medical and mathematical backgrounds
Based on famous modules from the Socrates educational project Eudipharm of the EC
Innovative because of its explanatory rather than mathematical approach
Emphasizes non-classical but increasingly frequently used methods such as equivalence testing, interaction assessment and analysis of genetic data
Not equivalent to any current textbook on statistics
In clinical medicine appropriate statistics has become indispensable to evaluate treatment effects. Randomized controlled trials are currently the only trials that truly provide evidence-based medicine. Evidence based medicine has become crucial to optimal treatment of patients. We can define randomized controlled trials by using Christopher J. Bulpitt’s definition “a carefully and ethically designed experiment which includes the provision of adequate and appropriate controls by a process of randomization, so that precisely framed questions can be answered”. The answers given by randomized controlled trials constitute at present the way how patients should be clinically managed. In the setup of such randomized trial one of the most important issues is the statistical basis. The randomized trial will never work when the statistical grounds and analyses have not been clearly defined beforehand. All endpoints should be clearly defined in order to perform appropriate power calculations. Based on these power calculations the exact number of available patients can be calculated in order to have a sufficient quantity of individuals to have the predefined questions answered. Therefore, every clinical physician should be capable to understand the statistical basis of well performed clinical trials. It is therefore a great pleasure that Drs. T. J. Cleophas, A. H. Zwinderman, and T. F. Cleophas have published a book on statistical analysis of clinical trials. The book entitled “Statistics Applied to Clinical Trials” is clearly written and makes complex issues in statistical analysis transparant.
Content Level »Research
Keywords »Analysis of variance - Measure - Regression analysis - data analysis - linear regression - statistics
Foreword
Chapter 1: Hypotheses, Data, Stratification
Chapter 2: The Analysis of Efficacy Data
Chapter 3: The Analysis of Safety Data
Chapter 4: Log Likelihood Ratio Tests for Safety Data Analysis
Chapter 5: Equivalence Testing
Chapter 6: Statistical Power and Sample Size
Chapter 7: Interim Analyses
Chapter 8: Clinical Trials Are Often False Positive
Chapter 9: Multiple Statistical Inferences
Chapter 10: The Interpretation of the P-Values
Chapter 11: Research Data Closer to Expectation than Compatible with Random Sampling
Chapter 12: Statistical Tables for Testing Data Closer to Expectation than Compatible with Random Sampling
Chapter 13: Principles of Linear Regression
Chapter 14: Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism
Chapter 15: Curvilinear Regression
Chapter 16: Logistic and Cox Regression, Markow Models, Regression with Laplace Transformations
Chapter 17: Regression Modeling For Improved Precision
Chapter 18: Post-Hoc Analysis in Clinical Trials, A Case For Logistic Regression Analysis
Chapter 19: Confounding
Chapter 20: Interaction
Chapter 21: Meta-Analysis, Basic Approach
Chapter 22: Meta-Analysis, Review and Update of Methodologies
Chapter 23: Crossover Studies with Continuous Variables
Chapter 24: Crossover Studies with Binary Responses
Chapter 25: Cross-Over Trials Should Not Be Used To Test Treatments with Different Chemical Class
Chapter 26: Quality-Of-Life Assessments in Clinical Trials
Chapter 27: Statistics for the Analysis of Genetic Data
Chapter 28: Relationship among Statistical Distributions
Chapter 29: Testing Clinical Trials for Randomness
Chapter 30: Clinical Trials Do Not Use Random Samples Anymore
Chapter 31: Clinical Data Where Variability Is More Important than Averages
Chapter 32: Testing Reproducibility
Chapter 33: Validating Qualitative Diagnostic Tests
Chapter 34: Uncertainty of Qualitative Diagnostic Tests
Chapter 35: Meta-Analyses of Qualitative Diagnostic Tests
Chapter 36: Validating Quantitative Diagnostic Tests
Chapter 37: Summary of Validation Procedures for Diagnostic Tests
Chapter 38: Validating Surrogate Endpoints of Clinical Trials
Chapter 39: Methods for Repeated Measures Analysis
Chapter 40: Advanced Analysis Of Variance, Random Effects and Mixed Effects Models
Chapter 41: Monte Carlo Methods for Data Analysis
Chapter 42: Physicians’ Daily Life and the Scientific Method
Chapter 43: Superiority-Testing
Chapter 44: Trend-Testing
Chapter 45: Odds Ratios and Multiple Regression, Why and How to Use Them
Chapter 46: Statistics Is No 'Bloodless' Algebra
Chapter 47: Bias Due to Conflicts of Interests, Some Guidelines
Appendix
Index