Learning objectives
Know how to set up a statistical analysis by taking a small clinical experimentation example; know how to draw conclusions using the tests learned
Course unit content
<br />GENERAL DETAILS <br />
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- Statistical universe <br />
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- Types of measurement, test applicability criteria <br />
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- The biological sample: extraction and sizing methodology <br />
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- Randomisation <br />
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- Frequency distributions: Histograms <br />
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- Normal Gaussian distribution <br />
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- Probability theory <br />
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DESCRIPTIVE STATISTICS <br />
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-Graphic representation of results <br />
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-Fundamental parameters: mean, independent observations, variance, standard deviation and standard error <br />
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-Confidence limits of the mean and relative clinical significance <br />
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-Percentiles <br />
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PARAMETRIC STATISTICS <br />
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-The zero hypothesis ( Ho ), types of error <br />
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-Mean comparison analysis: Student t test, analysis of the variance, <br />
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-Percentage comparison analysis: contingency tables, <br />
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Chi square <br />
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-Fisher’s exact test <br />
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-Correlation: linear regression, Scatchard plot <br />
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NON PARAMETRIC STATISTICS <br />
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- Free distribution data analysis methods <br />
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- Subjective assessment scales in clinical medicine <br />
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- Limits and advantages of non parametric tests <br />
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- Rank <br />
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CLINICAL RESEARCH METHODOLOGY <br />
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- Definition of normal and pathological parameters <br />
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- Clinical experimentation <br />
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-Clinical trials: blind and double blind <br />
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- Control, placebo, follow-up <br />
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? Informed consent <br />
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Bibliography
STANTON A. GLANTZ: Statistica per discipline Bio-mediche, McGraw-Hill <br />
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L. LISON: Statistica applicata alla biologia sperimentale, Casa Ed. Ambrosiana <br />
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Introduzione ai sistemi informatici (Third edition) ?D. Sciuto, G. Buonanno, W. Fornaciari, L. Mari, ?Mc Graw Hill, 2004 <br />
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