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.
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