Statistics

Not applicable

Theoretical:

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

Theoretical-practical:

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

The student must acquire knowledge of:

•  Analytical Methods and Techniques in Statistics that allow the understanding of different health phenomena.
•  Sample Estimation Method and its Test Potency according to the different research designs that the student can be confronted with in the medical, clinical and laboratory scope.
• Contact with Data Analysis software in Statistics.

The student must acquire skills of:

•     Sample Estimation on population / sample, experimental, cohort and case-control parameters.
•     Creation of “Databases”, manipulation of clinical and laboratory indicators (exams, harvests, analytical parameters, measurement scales, etc.) and interpretation of results.

The student must acquire skills of:

• Decision of Statistics Analysis Models for the study of real facts (prevalence / diagnosis), prediction of results (prognosis) and choice of indicators / indices.
• Validation of diagnostic and prognostic methods in the clinical field.
• Analysis and interpretation of results manipulated in statistical analysis software.

1.    General Concepts: Descriptive Statistics and Inductive Statistics; Sample Designs: Probabilistic/Random and Non-Probabilistic Models; Concepts of Population (Universe) and Sample.

2.    Data Reduction and definition of Measurement Scales; Descriptive Statistics Measures: data tabulation, measures of central and non-central tendency and dispersion; Distribution Measures: Symmetry, Flatness and Normal Distribution and their properties for inferential statistics.

3.    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.

4.    Inferential Statistics Measures: Point Estimation and Statistical Tests:
•    Statistical models – 2 paired samples: McNemar test; Cohen’s Kappa test; Wilcoxon T test; Signal Test; t-Student test.
•    Statistical models – 3 or more follow-up samples (longitudinal models): ANOVA test for repeated measures at Factor I and respective Multiple Comparison tests (Bonferroni test and Least Significant Difference test) and Effect Size estimation; Friedman Non-parametric ANOVA test and respective Multiple Comparisons test (Bonferroni test).
•    Statistical models – 1 sample: Student’s t-test; Adherence chi-square test.
•    Statistical models – 2 independent samples: Pearson’s Chi-square Test (Independence Chi-square); Pearson’s Chi-square Test with Yates’ Continuity Correction and Fisher’s test; Association Measures: Phi Coefficient, W Coefficient (Cramer), Pearson C Coefficient; Diagnostic Measurements: Sensitivity; Specificity; Predictive Values (positive and negative); Likelihood Ratio. Wilcoxon-Mann-Whitney test; t-Student Test for Independent Samples and Levene Test;
•    Statistical models – 3 or more independent samples: One-way ANOVA test respective Multiple Comparisons test (Bonferroni test; LSD test; Games-Howell test); Kruskal-Wallis non-parametric ANOVA test and respective Multiple Comparisons test (Dunn-Bonferroni test).
•    Statistical models – correlated samples: Pearson’s Linear Correlation Coefficient; Spearman’s Ordinal Correlation Coefficient.

5.    Introduction and Application of Statistical Analysis Software: Creation of Databases, simulation of clinical phenomena according to study designs (research). Application of statistical models, interpretation of results and their extrapolation to the population.

NAO

1. Primary Bibliography

1. Vet, H.C.; Terwee, C.B.; Mokkink, L.B.; Knol, D.L. Measurement in Medicine – Pratical Guide to Biostatistics and Epidemiology. Cambridge University Press, 7th Printing, United Kingdom, 2016.

2. Motulsky, H. “Intuitive Biostatistics – A Nonmathematical Guide to Statistical Thinking”. Completely Revised, Second Edition, Oxford University Press, New York, 2010.

3. Cunha, G.; Martins, M.R.; Sousa, R.; Oliveira, F.F. Estatística Aplicada às Ciências e Tecnologias da Saúde. Lídel: Lisboa, 2007.

4. Pestana, M.H.; Gageiro, J.N. Análise de Dados para Ciências Sociais – A complementaridade do SPSS. 4.ª Ed., (Revista e Aumentada), Edições Sílabo: Lisboa, 2005.

5. Vidal, P.M. “Estatística prática para as ciências da saúde”. Lidel, Lisboa, 2005.

6. Kirkwood, B., Sterne, J. Essentials of Medical Statistics. 2.nd edition. Wiley-Blackwell, 2001.

2. Secondary Bibliography

1. Mello, F.C.; Guimarães, R.C. Métodos Estatísticos para o Ensino e a Investigação nas Ciências da Saúde. Edições Sílabo: Lisboa, 2015.

2. Hall, A.; Neves, C.; Pereira, A. Grande Maratona de Estatística no SPSS. Escolar Editora: Lisboa, 2011.

3. Elizabeth Reis, Rosa Andrade, Teresa Calapez e Paulo Melo. Exercícios de Estatística Aplicada – Vol. 2 (3ª Edição revista e corrigida). Editor: Edições Sílabo, Edição: janeiro de 2021

4. Marôco, J. Análise Estatística com o SPSS Statistics. 8.ª Edição, Lisboa, 2012. Editor: ReportNumber, Edição: Março de 2021

5. Figueiredo, F. Estatística Descritiva e Probabilidades – Problemas Resolvidos e Propostos com Aplicações em R (2ª Edição). Editor: Escolar Editora; Edição: outubro de 2009

6. Oliveira, A. G. Bioestatística Descodificada: Bioestatística, Epidemiologia e Investigação. 2.ª Ed., Lidel: Lisboa, 2014.

7. Santos, C. Manual de Auto-Aprendizagem – Estatística Descritiva, Edições Sílabo: Lisboa, 2007.

8. Rosner, B. “Fundamentals of Biostatistics”. Sixth Edition, International Student Edition. THOMSON, Brooks/Cole, USA, 2006.

9. Levin, J.; Fox, J.A. “Estatística para Ciências Humanas”, 9.ª Edição, Pearson/Prentice Hall, São Paulo, 2004.