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Statistics applied to clinical trials

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目次 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
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 studies
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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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2009 English/International

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登録日 2020.06.27
更新日 2020.06.28