Titlebook: Statistics Applied to Clinical Trials; Ton J. Cleophas,Aeilko H. Zwinderman,Toine F. Cleo Book 20063rd edition Springer Science+Business M

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Multiple Statistical Inferences,ssment of such questions introduces the statistical problem of multiple comparison and multiple testing, which increases the risk of false positive statistical results, and thus increases the type-I error risk. In this chapter simple methods are discussed which can help to control this risk.

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Hypotheses, Data, Stratification,ded into IV phases (from phase I for initial testing to phase IV after release for general use), but scientific rules governing different phases are very much the same, and can thus be discussed simultaneously.

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The Interpretation of the P-Values,control of 0.000 is hardly compatible with random sampling. As it comes to well-balanced random sampling of representative experimental data, nature will be helpful to provide researchers with results close to perfection.

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The Analysis of Efficacy Data of Drug Trials,e the following statistical techniques:.Comparing proportions of responders or proportions of survivors or patients with no events involves binomial rather than normal distributions and requires a completely different approach. It requires a chi-square test, or a more complex technique otherwise clo

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Equivalence Testing,simply because the sample size is too small. Equivalence testing is particularly important for studying the treatment of diseases for which a placebo control would unethical. In the situation a new treatment must be compared with standard treatment. The latter comparison is at risk of finding little

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