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.
From the reviews of the fifth edition:"`Statistics Applied to Clinical Studies, 5th Edition' clearly explains most anything that might be worth knowing about clinical research statistics. The content is accessible to non-staticians and also has the breadth and depth to interest professional biostaticians." (Norman M. Goldfarb, Journal of Clinical Research Best Practices, Vol. 9 (2), February, 2013)