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 “Bloodless” Algebra -- Bias Due to Conflicts of Interests, Some Guidelines.

The previous three editions of this book, rather than having been comprehensive, concentrated on the most relevant aspects of statistical analysis. Although well-received by students, clinicians, and researchers, these editions did not answer all of their questions. This updated and extended edition has been written to serve as a more complete guide and reference-text to students, physicians, and investigators, and, at the same time, preserves the common sense approach to statistical problem-solving of the previous editions. 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 little sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were being 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 implemented and lead to trials after 1970 being of significantly better quality than before. The past decade 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 organs, including ethic committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. The present book not only explains classical statistical analyses of clinical trials, but also addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, meta-analyses, and provides a framework of the best statistical methods currently available for such purpose. This book is not only useful for investigators involved in the field of clinical trials, but also for students and physicians who wish to better understand the data of trials as published currently.