Opis

Explains classical statistical analysis of clinical trials. This book addresses relatively novel issues, including equivalence testing, interim analysis, sequential analysis, meta-analysis, and provides a framework of the best statistical methods available for such purpose. It is suitable for investigators in the field of clinical trials.ForewordChapter 1: Hypotheses, Data, StratificationChapter 2: The Analysis of Efficacy DataChapter 3: The Analysis of Safety DataChapter 4: Log Likelihood Ratio Tests for Safety Data AnalysisChapter 5: Equivalence TestingChapter 6: Statistical Power and Sample SizeChapter 7: Interim AnalysesChapter 8: Clinical Trials Are Often False PositiveChapter 9: Multiple Statistical InferencesChapter 10: The Interpretation of the P-ValuesChapter 11: Research Data Closer to Expectation than Compatible with Random SamplingChapter 12: Statistical Tables for Testing Data Closer to Expectation than Compatible with Random SamplingChapter 13: Principles of Linear RegressionChapter 14: Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, SynergismChapter 15: Curvilinear RegressionChapter 16: Logistic and Cox Regression, Markow Models, Regression with Laplace TransformationsChapter 17: Regression Modeling For Improved PrecisionChapter 18: Post-Hoc Analysis in Clinical Trials, A Case For Logistic Regression AnalysisChapter 19: ConfoundingChapter 20: InteractionChapter 21: Meta-Analysis, Basic ApproachChapter 22: Meta-Analysis, Review and Update of MethodologiesChapter 23: Crossover Studies with Continuous VariablesChapter 24: Crossover Studies with Binary ResponsesChapter 25: Cross-Over Trials Should Not Be Used To Test Treatments with Different Chemical ClassChapter 26: Quality-Of-Life Assessments in Clinical TrialsChapter 27: Statistics for the Analysis of Genetic DataChapter 28: Relationship among Statistical DistributionsChapter 29: Testing Clinical Trials for RandomnessChapter 30: Clinical Trials Do Not Use Random Samples AnymoreChapter 31: Clinical Data Where Variability Is More Important than AveragesChapter 32: Testing ReproducibilityChapter 33: Validating Qualitative Diagnostic TestsChapter 34: Uncertainty of Qualitative Diagnostic TestsChapter 35: Meta-Analyses of Qualitative Diagnostic TestsChapter 36: Validating Quantitative Diagnostic TestsChapter 37: Summary of Validation Procedures for Diagnostic TestsChapter 38: Validating Surrogate Endpoints of Clinical TrialsChapter 39: Methods for Repeated Measures AnalysisChapter 40: Advanced Analysis Of Variance, Random Effects and Mixed Effects ModelsChapter 41: Monte Carlo Methods for Data AnalysisChapter 42: Physicians' Daily Life and the Scientific MethodChapter 43: Superiority-TestingChapter 44: Trend-TestingChapter 45: Odds Ratios and Multiple Regression, Why and How to Use ThemChapter 46: Statistics Is No "Bloodless" AlgebraChapter 47: Bias Due to Conflicts of Interests, Some GuidelinesAppendixIndex