Statistics

Not applicable

Theoretical:

• Use of expository-active methodology easy to understand by the students.
• The display will focus on the identification and understanding of the basic concepts of Statistics and its relation with the Health field.
• Display of methods and statistical techniques according to the clinical reality and its transposition into the computer environment.

Theoretical-practical:

• Focuses on the application of knowledge learned in theory using statistical analysis Software (IBM SPSS Statistics).
• The student will benefit from a practical component in statistical analysis Software from the formation of databases, aggregation, transformation and its manipulation.
• Application of different models of statistical analysis using specialized Software.

The student must acquire knowledge of:

• Analytical Methods and Techniques in Statistics that allows the understanding of different phenomena in health.
• Method of Sampling Estimation and its Test Power for different research designs that the student can be confronted within a medical, clinical and laboratory context.
• Different Data Analysis in Statistics Software.

The student must acquire skills of:

• Estimation Samples on different parameters in population/samples, experimental groups, cohort and casecontrol.
• Creation of “databases”, handling different clinical and laboratory indicators (examination, harvest, analytical parameters, measurement scales, etc.) and interpretation of results.

The student must acquire skills of:

• Decision of Statistical Analysis Models for the identification of actual events prevalence/diagnosis), prediction of outcome (prognosis) and choice of indicators/indexes for the understanding of reality.
• Validation of methods for diagnosis and prognosis within clinical context.
• Analysis and interpretation of results manipulated in statistical analysis Software.

Part I:

General Concepts: Descriptive Statistics and Inferential Statistics; Concepts of Population (N) and Sample (n), Representativeness and Selection Criteria. Sampling Methods: Probabilistic/Random and Non-Probabilistic Models. Data Reduction and definition of Measurement Scales.

Part II:

Introduction to creating databases and specialized software for managing and processing data. Descriptive Statistics Measures: data tabulation, measures of central and non-central tendency and dispersion; Distribution Measures: Symmetry, Flatness and Normal Distribution. Practical application of concepts (calculation and transposition to software – IBM SPSS Statistics).

Part III:

Hypothesis Testing: Null hypothesis and statistical hypothesis; Univariate Hypothesis and Bivariate Hypothesis and the Decision Rule (significance level); Type I Error (1st Kind Error) and Type II Error (2nd Kind Error). Family of Parametric Tests and Non-Parametric Tests. Inferential Statistics Measures: Point Estimation and Statistical Tests depending on the type of study/Samples (Decision Trees).

Part IV:

Models for Paired/Related Samples: McNemar test (X2MC) and its variant with Yates Continuity Correction; Cohen’s Kappa test (K); Wilcoxon Test (T); t-Student test (t); ANOVA test for repeated measures at Factor I (F) and respective Multiple Comparison tests (t test via Fisher’s Least Significant Difference and t test via Bonferroni); Friedman Nonparametric ANOVA test (X2r) and respective Multiple Comparisons test (Corrected Bonferroni Test).

Models for Independent Samples: Pearson’s Chi-square Test (X2MC) and its variant of Yates’ Continuity Correction as well as the estimation of Residuals (Standardized Adjusted) and respective Coefficients of Association: Phi and Cramer’s V (V); Wilcoxon-Mann-Whitney (U) test; t-Student test (t); Levene Test (W); One-Factor ANOVA test (F) respective Multiple Comparisons test; Kruskal-Wallis non-parametric ANOVA test (H) and respective Multiple Comparisons test (Dunn-Bonferroni).

Correlation Models: Introduction to Covariance (Cov); Graphic representation of the Dispersion Diagram; Pearson’s Linear Correlation Coefficient (r); Spearman’s Ordinal Correlation Coefficient (rho).

Application of statistical models through simulation of clinical phenomena, interpretation of results and their extrapolation to the population.

NAO