Study designs (examples of observational and experimental studies; secondary studies or literature reviews)
Statistical measures derived from different studies (diagnostic accuracy, disease occurrence, relative risk, efficacy)
Variables: types (explanatory, control, outcome) and measurement scales (nominal, ordinal, interval, rational).
Data quality: accuracy, precision and data management: data matrix, observation unit, use of electronic spreadsheets
Data tabulation and graph construction: pie charts, bar charts, histograms, cumulative curve, scatter plots, dot plot, box and whisker plot, survival curve
Measures of central tendency and dispersion: centiles, mean, range, standard deviation, interquartile range.
Probability models, random variables, expected values (Bernoulli, Binomial, Poisson, hypergeometric, Gaussian)
Random sampling and sampling distributions of statistics (mean, difference of means, proportion, difference of proportions, ratio of proportions, odds ratio, rates, rate difference, rate ratio)
Null hypothesis and alternative hypothesis, rejection area, type I and type II errors, sample size and power of the studies
Confidence intervals (means, proportions, rates; difference of means, difference of proportions, difference of rates; ratio of proportions, ratio of rates, odds ratios)
Data analysis exercises:
Independent and paired samples: comparison of means (Student's t test and z test, confidence interval of the difference between two means); contingency tables analysis: 2x2; Rx2; RxC; RxCxK (Chi square test; confidence interval of association measures for etiological, diagnostic accuracy and intervention studies)
Linear regression analysis and deviance decomposition: the least squares method for estimating parameters.
Confounding factors. Controlling for confounding variables: stratification (Mantel-Haenszel test) and regression models (regression analysis, logistic regression analysis)
Introduction to survival analysis: Kaplan-Meier method, Cox regression and Hazard Ratio (HR) estimation
Prerequisites for admission
Basic mathematics and algebra knowledge are required
The course is delivered through lectures focused on the concepts and the practical use of elementary methods of statistical analysis, with particular attention to the interpretation of study results and applications in the context of etiological, diagnostic, therapeutic clinical decision. Particular attention will be paid to the critical evaluation of the quality of the different types of study (etiological, diagnostic, efficacy, systematic literature review).
Martin Bland (2019) "Statistica medica" Maggioli Editore
Supporting files are available on Ariel website: handouts (pdf), slides (pdf), spreadsheets (xlsx), commented exercises (pptx), scientific articles (pdf)
Assessment methods and Criteria
The examination consists of a 2-hour written test including practical exercises, formulated as open questions or multiple choice questions. Use of pocket calculator and of statistical tables is allowed. A two-step assessment method will be followed. First, some specific exercises (clearly specified) must be solved to reach the sufficiency threshold, without their solution students will not pass the exam. Second, a further series of exercises that must be solved to obtain the final mark. after the correction of the test, the solutions and the mark of each assignment will be communicated through the university website.