Medical Statistics for Pharmacy

Esci dai Frame

MEDICAL STATISTICS FOR PHARMACY

Programma del Corso

Introduction to Statistics and the use of Statistics in Medicine (Evidence-Based Medicine / Nursing / Prevention). Quantitative knowledge of phenomena with variability.

Elements of descriptive statistics. Main concepts and terminology; classification of variables and their coding. Frequency distributions, syntheses and graphical representations; position and variability indexes (arithmetic mean, weighted mean, median and quantiles, ranges, standard deviation, variance and variation coefficient). Statistical indexes in relation with the shape of the distribution (symmetry, skewness, multiple modes).

Elements of probability theory. Events and definition of probability; conditional probability; basic rules of probability theory; Bayes formula. Applications. Diagnostic test (concept of sensitivity, specificity, predictive value of the test). Main probability distributions and their use in applications: Binomial, Poisson, Normal.

Elements of frequentist statistical inference. Framework and repeated sampling principle; the distribution of the sample mean. Point estimation; main properties of estimators. Interval estimation via confidence intervals. concepts and basic elements of statistical testing; type I and II error, rejection regions, significance (p-value). Examples: T-test for the mean and the proportion. The analysis of associations. Cross-tables, Risk Ratio, Chi-Squared test. (Z-) T-test for mean. Linear associations: correlation and regression. Notions of further methods. Interpretation: confounding.
Suggested textbooks:
• Medical statistics at a glance, by A Petrie & C Sabin, Ed. Wiley-Blackwell
• An introduction to medical statistics, by M Bland, Oxford Medical Publications

	MEDICAL STATISTICS FOR PHARMACY

Programma del Corso

Introduction to Statistics and the use of Statistics in Medicine (Evidence-Based Medicine / Nursing / Prevention). Quantitative knowledge of phenomena with variability. Elements of descriptive statistics. Main concepts and terminology; classification of variables and their coding. Frequency distributions, syntheses and graphical representations; position and variability indexes (arithmetic mean, weighted mean, median and quantiles, ranges, standard deviation, variance and variation coefficient). Statistical indexes in relation with the shape of the distribution (symmetry, skewness, multiple modes). Elements of probability theory. Events and definition of probability; conditional probability; basic rules of probability theory; Bayes formula. Applications. Diagnostic test (concept of sensitivity, specificity, predictive value of the test). Main probability distributions and their use in applications: Binomial, Poisson, Normal. Elements of frequentist statistical inference. Framework and repeated sampling principle; the distribution of the sample mean. Point estimation; main properties of estimators. Interval estimation via confidence intervals. concepts and basic elements of statistical testing; type I and II error, rejection regions, significance (p-value). Examples: T-test for the mean and the proportion. The analysis of associations. Cross-tables, Risk Ratio, Chi-Squared test. (Z-) T-test for mean. Linear associations: correlation and regression. Notions of further methods. Interpretation: confounding. Suggested textbooks: • Medical statistics at a glance, by A Petrie & C Sabin, Ed. Wiley-Blackwell • An introduction to medical statistics, by M Bland, Oxford Medical Publications