Statistics Applied to Clinical Trials [electronic resource] / edited by Ton J. Cleophas, Aeilko H. Zwinderman, Toine F. Cleophas, Eugene P. Cleophas.

Hypotheses, Data, Stratification -- The Analysis Of Efficacy Data -- The Analysis of Safety Data -- Log Likelihood Ratio Tests For Safety Data Analysis -- Equivalence Testing -- Statistical Power And Sample Size -- Interim Analyses -- Controlling The Risk of False Positive Clinical Trials -- Multiple Statistical Inferences -- The Interpretation of The P-Values -- Research Data Closer To Expectation Than Compatible With Random Sampling -- Statistical Tables For Testing Data Closer To Expectation Than Compatible With Random Sampling -- Principles Of Linear Regression -- Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism -- Curvilinear Regression -- Logistic and Cox Regression, Markow Models, Laplace Transformations -- Regression Modeling for Improved Precision -- Post-Hoc Analyses in Clinical Trials, a Case for Logistic Regression Analysis -- Confounding -- Interaction -- Meta-Analysis, Basic Approach -- Meta-Analysis, Review and Update of Methodologies -- Crossover Studies with Continuous Variables -- Crossover Studies with Binary Responses -- Cross-Over Trials Should not be Used to Test Treatments with Different Chemical Class -- Quality-of-Life Assessments in Clinical Trials -- Statistical Analysis of Genetic Data -- Relationship Among Statistical Distributions -- Testing Clinical Trials for Randomness -- Clinical Trials do not Use Random Samples Anymore -- Clinical Data Where Variability is More Important than Averages -- Testing Reproducibility -- Validating Qualitative Diagnostic Tests -- Uncertainty of Qualitative Diagnostic Tests -- Meta-Analysis of Diagnostic Accuracy -- Validating Quantitative Diagnostic Tests -- Summary of Validation Procedures for Diagnostic Tests -- Validating Surrogate Endpoints of Clinical Trials -- Methods for Repeated Measures Analysis -- Advanced Analysis of Variance, Random Effects and Mixed Effects Models -- Monte Carlo Methods -- Physicians Daily Life and the Scientific Method -- Clinical Trials: Superiority-Testing -- Trend-Testing -- Odds Ratios and Multiple Regression Models, Why and How to Use Them -- Statistics is no ǣBloodlessǥ Algebra -- Bias Due to Conflicts of Interests, Some Guidelines.

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. Bulpitts definition ǣa 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 answeredǥ. 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 ǣStatistics Applied to Clinical Trialsǥ is clearly written and makes complex issues in statistical analysis transparant.

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