The authors have both medical and mathematical backgrounds

Based on famous modules from the Socrates educational project

Eudipharm of the EC Innovative because of its explanatory rather than mathematical approach

Emphasizes non-classical but increasingly frequently used methods such as equivalence testing, interaction assessment and analysis of genetic data

Not equivalent to any current textbook on statistics

Preface. Foreword.-1 Hypotheses, data, stratification.-2 The analysis of efficacy data.-3 The analyis of safety data.-4 Log likelihood ratio tests for safety data analysis.-5 Equivalence testing.- 6 Statistical power and sample size.-7 Interim analyses.-8 Clinical trials are often false positive.-9 Multiple statistical inferences.-10 The interpretation of the p-values.-11 Research data closer to expectation than compatible with random sampling.-12 Statistical tables for testing data closer to expectation than compatible with random sampling.-13 Dispersion issues.-14 Linear regression, basic approach.-15 Linear regression for assessing precision, confounding, interaction, basic approach.-16 Curvilinear regression.-17 Logistic and cox regression, markow models, regression with laplace transformations.-18 Regression modeling for improved precision.-19 Post-hoc analysis in clinical trials, a case for logistic regression analysis.-20 Multistage regression.-21 Categorical data.-22 Missing data.-23 Poisson regression for event analysis.-24 More on non linear relationships, splines.-25 Multivariate modeling.-26 Bhattacharya modeling.-27 Trend-testing.-28 Confounding.-29 Propensity score matching.-30 Interaction.-31 Time-dependent factor analysis.-32 Meta-analysis, basic approach.-33 Meta-analysis, review and update ofmethodologies.-34 Meta-regression.-35 Crossover studies with continuous variables.-36 Crossover studies with binary responses.-37 Cross-over trials should not be used to test treatments with different chemical class.-38 Quality-of-life assessments in clinical.-39 Item response modeling.-40 Statistics for the analysis of genetic data.-41 Relationship among statistical.-42 Testing clinical trials for randomness.-43 Clinical trials do not use random samples anymore.-44 Clinical data where variability is more important than averages.-45 Testing reproducibility.-46 Validating qualitative diagnostic tests.-47 Uncertainty of qualitative diagnostic tests.-48 Meta-analyses of qualitative diagnostic tests.-49 C-statistics versus logistic regression for assessing the performance of qualitative diagnostic tests.-50 Validating quantitative diagnostic tests.-51 Summary of validation procedures for diagnostic tests.-52 Validating surrogate endpoints of clinical trials.-53 Binary partitioning.-54 Methods for repeated measures analysis.-55 Mixed linear models for repeated measures.-56 Advanced analysis of variance, random effects and mixed effects models.-57 Monte Carlo methods for data analysis.-58 Artificial intelligence.-59 Fuzzy logic.-60 Physicians' daily life and the scientific method.-61 Incidence analysis and the scientific method.-62 Superiority-testing.-63 Non-inferiority testing.-64 Time series.-65 Odds ratios and multiple regression, why and how to use them.-66 Statistics is no "bloodless" algebra.-67 Bias due to conflicts of interests, some guidelines. Appendix .Index.