Dental Clinical Informatics and Statistics 1

Data

Official data in SubjectManager for the following academic year: 2024-2025

Topic

The aim of the course is: reviewing and processing data and characterization using graphical and numerical tools, probability and statistical conclusion/decision, basic statistical methods commonly used in medicine and medical practice.

The other goal is introducing and practising the basics of statistical thinking. The skills and competences can be developed with statistics in scientific background and daily work of medicine. Analytical and critical thinking are of high importance in medical decision making, and there is also an opportunity to develop these skills within the subject.

Lectures

• 1. Introduction (Biometrics and Medical Sciences). Probability and relative frequency. - Kőnigné Péter Anikó
• 2. Variables and its representation. - Kőnigné Péter Anikó
• 3. Probability calculation and discrete distributions. - Dergez Tímea
• 4. Description statistics. - Dergez Tímea
• 5. Continuous probability distributions-normal distribution. - Kőnigné Péter Anikó
• 6. Statistical estimations. Confidence interval for the expected value. - Dergez Tímea
• 7. Principle of hypothesis testing. The one sample and the paired samples t tests. - Kőnigné Péter Anikó
• 8. The confidence interval and the hypothesis testing. Type I and type II errors. - Dergez Tímea
• 9. The independent samples t test. The power of the test. - Kőnigné Péter Anikó
• 10. The non-parametric tests: Sign-, Wilcoxon-, Mann-Whitney tests. - Dergez Tímea
• 11. Linear regression and correlation. - Dergez Tímea
• 12. The evaluation of frequency data: Chi-squared test and Fisher's exact test. Special applications. - Kőnigné Péter Anikó
• 13. The principle of the ANOVA. - Dergez Tímea
• 14. Summary of the hypothesis testing methods. - Kőnigné Péter Anikó

Practices

• 1. Relative frequency and probability, thinking models (deterministic and stochastic models).
• 2. Types of data and their representation.
• 3. Probability calculation-Binomial and Poisson distribution.
• 4. Exploring data by numbers - descriptive statistics
• 5. Normal distribution. The distribution of the sample mean.
• 6. Estimations. The confidence interval of the expected value.
• 7. 1st midterm test. The hypothesis testing - the one sample (and the paired samples) t tests.
• 8. Two independent samples t test. The Type one and Type two errors.
• 9. Nonparametric tests.
• 10. The linear regression and correlation.
• 11. Contingency tables - the chi-squares test, Fisher's exact test.
• 12. Summary.
• 13. Practice.
• 14. 2nd midterm test.

Seminars

Reading material

Obligatory literature

Literature developed by the Department

Moodle, PotePedia files

Notes

Moodle electronic notes

Joseph Belágyi: Medical Biometry

Sára Jeges: Biometry

L. Pótó: Biometrics. Workbook for the Practices, Pécs, 2020

Recommended literature

1. D.S. Moore, G.P. McCabe: Introduction to the Practice of Statistics, 5th ed., W.H. Freeman 2005

2. D. Yates, D.S. Moore, D.S. Starnes: The Practice of Statistics (TI-83/89 Graphing Calculator Enhanced) 2/e, 2003, W.H. Freeman

3. W.G. Rees: Essential Statistics, Chapman and Hall, 1992

Conditions for acceptance of the semester

There are two midterm tests during the semester with at least 60% result and short tests at the beginning of all practices. Only three absences are allowed.

Exam: a test on an electronic platform with a minimum score of 60% or an oral test with a computer-based problem and a theoretical question. In each of these parts should be completed at least satisfactory for a successful exam.

Mid-term exams

There are two midterm tests during the semester with at least 60% result and short tests at the beginning of all practices. Only three absences are allowed.

Making up for missed classes

Retake class.

Exam topics/questions

Themes of theoretical part:

1. The main goals / potential results of learning biometrics/biostatistics.

2. The key feature of the statistical thinking - The probability.

3. The idea of the probability distribution - discrete distributions.

4. The basic principles of statistical thinking - from the data to the decision: size of the sample, representativity, lurking variables.

5. Types of the data (variables) and displaying them with graphs.

6. Graphical and numerical characterization of the sample and the population

7. Numerical description of continuous data: five number and three number descriptions.

8. The idea of the probability distribution - continuous distributions.

9. The normal distribution, the central limit theorem.

10. Statistical estimation: point estimation and interval estimation.

11. The confidence interval of the population mean

12. The basic idea of hypothesis testing

13. The one sample and the paired t-test

14. Comparing the confidence interval and the hypothesis testing.

15. The risk of errors and the power of a test.

16. The two (independent) samples t-test.

17. Connection between two variables - in case of continuous variables.

18. Connection between two variables - in case of categorical variables.

19. Special application of the contingency table, qualification of a diagnostic test (sensitivity, specificity, predictive values).

20. Nonparametric tests in case of one-, paired sample

21. Nonparametric tests in case of independent samples.

22. The principle of the Analysis of Variance (ANOVA).

Examiners

• Dergez Tímea
• Kőnigné Péter Anikó
• Makszin Lilla

Instructor / tutor of practices and seminars

• Dergez Tímea
• Kőnigné Péter Anikó
• Makszin Lilla