The authors have taught statistics and given statistics workshops in France and the Netherlands for almost 4 years by now. Their material, mainly on power point, consists of 12 lectures that have been continuously changed and improved by interaction with various audiences. For the purpose of the current book simple English text has been added to the formulas and figures, and the power points sheets have been rewritten in the format given by Kluwer Academic Publishers. Cartoons have been removed, since this is not so relevant for the transmission of thought through a written text, and at the…mehr
The authors have taught statistics and given statistics workshops in France and the Netherlands for almost 4 years by now. Their material, mainly on power point, consists of 12 lectures that have been continuously changed and improved by interaction with various audiences. For the purpose of the current book simple English text has been added to the formulas and figures, and the power points sheets have been rewritten in the format given by Kluwer Academic Publishers. Cartoons have been removed, since this is not so relevant for the transmission of thought through a written text, and at the end of each lecture (chapter) a representative number of questions and exercises for self-assessment have been added. At the end of the book detailed answers to the questions and exercises per lecture are given. The book has been produced with the same size and frontpage as the textbook "Statistics Applied To Clinical Trials" by the same authors and edited by same publishers ( 2nd Edition, DordrechtiBostonlLondon, 2002), and can be applied together with the current self-assessment book or separately. The current self-assessment book is different from the texbook, because it focuses on the most important aspects rather than trying to be complete. So, it does not deal with all of the subjects assessed in the texbook. Instead, it repeats on and on the principle things that are needed for every analysis, and it gives many examples that are further explained by arrows in the figures.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1 / Introduction to the Statistical Analysis of Clinical Trials, Continuous Data Analysis.- 2 / Equivalence Testing.- 3 / Power, Sample Size.- 4 / Proportional Data Analysis, Part I.- 5 / Proportional Data Analysis, Part II.- 6 / Meta-Analysis.- 7 / Interim-Analyses.- 8 / Multiple Testing.- 9 / Principles of Linear Regression.- 10 / Subgroup Analysis Using Regression Modeling.- 11 / Relationship Among Statistical Distributions.- 12 / Statistics is not Bloodless Algebra.- 13 / Bias Due to Conflicts of Interests, Some Guidelines.- Statistical Tables.- Answers to Questions and Exercises.
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.
1 / Introduction to the Statistical Analysis of Clinical Trials, Continuous Data Analysis.- 2 / Equivalence Testing.- 3 / Power, Sample Size.- 4 / Proportional Data Analysis, Part I.- 5 / Proportional Data Analysis, Part II.- 6 / Meta-Analysis.- 7 / Interim-Analyses.- 8 / Multiple Testing.- 9 / Principles of Linear Regression.- 10 / Subgroup Analysis Using Regression Modeling.- 11 / Relationship Among Statistical Distributions.- 12 / Statistics is not Bloodless Algebra.- 13 / Bias Due to Conflicts of Interests, Some Guidelines.- Statistical Tables.- Answers to Questions and Exercises.
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.
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