Cleophas T.J. et al. Statistics Applied to Clinical Trials

Springer-Verlag, 2009. - 584 p. - ISBN: 1402095228.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 the hypotheses to be tested. This phenomenon was attributed to low sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were 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, and so on. Such flaws, mainly of a technical nature, have been largely corrected and led to trials after 1970 being of significantly higher quality. The past decade has focused, in addition to technical aspects, on the need for circumspection in the planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial organs, including ethics committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. This book not only explains classical statistical analyses of clinical trials, but also addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, and meta-analyses, and provides a framework of the best statistical methods currently available for such purposes. This book is not only useful for investigators involved in the field of clinical trials, but also for all physicians who wish to better understand the data of trials as currently published.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
Clinical Trials Are Often False Positive
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, Regression with Laplace Transformations
Regression Modeling For Improved Precision
Post-Hoc Analysis 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
Statistics for the 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-Analyses of Qualitative Diagnostic Tests
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 for Data Analysis
Physicians’ Daily Life and the Scientific Method
Superiority-Testing
Trend-Testing
Odds Ratios and Multiple Regression, Why and How to Use Them
Statistics Is No "Bloodless" Algebra
Bias Due to Conflicts of Interests, Some Guidelines