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Mathematics - Probability Theory and Stochastic Processes | Statistics Applied to Clinical Trials

Statistics Applied to Clinical Trials

Cleophas, T.J., Zwinderman, A.H., Cleophas, T.F., Cleophas, E.P.

4th ed. 2009, XXII, 562p.

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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

Related subjects » Biomedical Sciences - Life Sciences, Medicine & Health - Probability Theory and Stochastic Processes - Statistics

Table of contents 

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

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