Methods of Experimental and Technological Research

Using Excel for basic statistics.
Statistics program R.
Statistics application with R.
Definition of quality.
Quality management.
Legislation related to quality controls.
Statistical methods for quality control.
Use of clinical G2.

Computer network services.
Network classification.
General structure of network architectures, introduction to protocols.
The transmission media.
Local networks.
Network level: routing and IP protocol.
Transport level: TCP and UDP.
Level of TCP / IP applications.
Microsoft protocols.

1. Evidence-Based Medicine.
1.1. Hierarchy of studies on diagnostic tests.
2. Diagnostic performance measures.
2.1. The results of a survey compared to a standard reference.
2.2. Sensitivity and specificity.
2.3. Predictive values, diagnostic accuracy and influence of disease prevalence.
2.4. Bayes theorem or subjective probability or conditional probability and likelihood ratio.
2.5. Discriminant thresholds and ROC curves.
3. Overdiagnosis.
4. Study design, systematic reviews and levels of evidence.
4.1. Phases 1, 2, 3 and 4 of the pharmacological research.
4.2. Classification of studies.
4.3. Experimental studies and control group.
4.4. Randomized trials.
4.4. Observational studies.
4.5. Sample size.
4.6. Systematic reviews (metanalysis).
4.7. Levels of evidence.
5. How to write a scientific radiological work.
5.1. Are informed consent and approval by the Ethics Committee always necessary?
5.2. Title, running title and Title page.
5.3. The four-section diagram, their dimensions and the editing sequence.
5.4. "Introduction": why did you do it?
5.5. "Materials and methods": what did you do and how did you do it?
5.6. «Results»: what did you find?
5.7. "Discussion": what is the meaning of your results?
5.8. "References."
5.9. «Abstract» and «Key words».

1. Variables and measurement scales, normal distribution and confidence intervals.
1.1. Variables and measurement scales.
1.1.1. Categorical variables.
1.1.2. Discrete numerical variables.
1.1.3. Continuous numeric variables.
1.2. Measurement scales.
1.3. The Gauss distribution.
1.4. Notes on descriptive statistics.
1.5. Indices of central tendency.
1.6. Dispersion around the central tendency: variance and standard deviation.
1.7. Standard error of the average.
1.8. The confidence intervals.
2. Null hypothesis, significance and statistical power.
2.1. Hypothesis null and principle of falsifiability.
2.2. Significance threshold, type I error and type II error.
2.3. Statistical power.
2.4. Why 0.05?
2.5. Interpreting the p.
3. Parametric and non-parametric statistics.
3.1. Parametric statistics.
3.1.1. The basics of parametric statistics.
3.1.2 Comparison between two sample averages: Student's t test.
3.1.3 Comparison of three or more sample averages: the analysis of variance.
3.1.4. ANOVA for independent and dependent groups.
3.1.5 The parametric statistics in radiology.
3.2. Non-parametric statistics.
3.2.1 A sample with two dependent or coupled measurements.
3.2.2. Variables measured on a dichotomous scale.
3.2.3. Variables measured on ordinal scale.
3.2.4. Variables measured on an interval or rational scale.
3.2.5 Two independent samples.
3.2.6. Variables measured on nominal or ordinal scale.
3.2.7. Variables measured on an interval or rational scale.
3.2.8. Three or more (k) dependent samples.
3.2.9. Variables measured on a dichotomous scale.
3.2.10. Variables measured on ordinal, interval or rational scale.
3.2.11 Three or more (k) independent samples.
3.2.12. Data measured on nominal or ordinal scale.
3.2.13. Variables measured on an interval or rational scale.
3.2.14. Considerations on non-parametric tests.
4. Correlation and linear regression.
4.1. Association and causation.
4.2. Correlation between continuous variables.
4.3. Interpretation of the correlation coefficient.
4.4. Significance test.
4.5. Rank correlation.
4.6. Linear regression.
4.6.1. The calculation of the coefficients.
4.7. Interpretation of the regression line.
4.8. Limitations on the use of linear regression.
5. Reproducibility: intraobserver and interobserver variability.
5.1. Sources of variability.
5.2. Why is it important to know the variability of the measurements?
5.3. The intra- and interobserver variability for continuous variables. The Bland-Altman method.
5.4. Interpretation of the results of the Bland-Altman method.
5.5. Intra- and interobserver variability for categorical variables: Cohen's kappa.