• Print

Medical Statistics
academic year 2019/2020
2nd semester

obligatory course

COURSE DATA

Year/semester: 1st year, 2nd semester
Lecture: 1 hours/ week, course code: AOK-KUA505, 1 credit
Practical/Seminar: 2 hours/ week, course code: AOK-KUA506, 2 credits

AIM OF THE COURSE

The aim of the course is to provide basic practical knowledge of biostatitsics, the use and interpretation of the most frequently used basic biostatistical methods used in medical research with the use of a statistical software. With conceptual understanding of data and data collection, we introduce techniques of data processing, representation and interpretation. We cover topics of trend analysis, use of hypotheses, frequently used statistical tests and their applications. Students will be able to state hypotheses according to given experimental design, formulate the data base, characterise the distribution of variables according to their type. Students will know the methods of the most often used hypothesis tests, they will be able to find the appropriate methods to test their hypotheses, and interpreting the results of computer programs and/or scientific papers. They will be able to decide when to ask for the help of a statistician.


TOPICS

  1. Data description. Types of data, displaying data. Sample characteristics. (categorical and continuous variables, absolute and relative frequency, bar chart, pie chart, histogram; mean, median, mode, range, quartiles, variance, standard deviation, mean-error chart, box diagram). Population, statistical sample.
  2. The basics of probability theory I. The concept of probability, rules of probability calculus. Probability and odds. Statistical estimation, confidence interval. Odds ratio and 95% confidence interval.
  3. The basics of probability theory II. Conditional probability, 2x2 tables, diagnostic tests, measures of accuracy. The distribution of categorical variables, expected value and variance.
  4. Notable distributions: the binomial distribution. Continuous distributions: the normal distribution. Standardisation, formula of the binomial test as a special case of standardisation.
  5. The central limit theorem, the standard error of mean. Confidence interval for the population-mean. The use of Student’s t-table.
  6. Statistical inference, one-sample t-test. Significance test by confidence interval, t-statistics or p-value. The binomial test.
  7. T-tests (one-sample, paired, Student and Welch two-sample t-test )
  8. Statistical errors, one-and two tailed tests, analysis of variance (principle of one-way ANOVA, F-test, pairwise comparisons).
  9. Correlation-regression analysis. Hypothesis tests for the coefficient of correlation and regression. Regression using transformations.
  10. The chi-squared test for independence (assumptions, Fisher exact test)
  11. Nonparametric methods based on ranks (Wilcoxon-test, Mann-Whitney test, Kruskal-Wallis test, rank correlation)
  12. 2 by 2 tables in epidemiology (measuring agreement using Cohen-Kappa, relative risk, odds ratio)
  13. Survival analysis: life tables, Kaplan-Meier method.
  14. Summary

 

REQUIREMENTS

Attendance of the lectures is strongly recommended; downloading the lecture slides cannot substitute for the participation at the lecture. The course ends in an end-semester examination.
The lectures are complemented by a practical course whose aim is to help students reach a deeper understanding of the lecture material.
Examination requirements

  • Students who fail to meet the requirements of practice (no mark or mark 1) cannot take the examination.

The end-semester exam:

  • Students take a computer-aided multiple-choice test examination at the end of the semester based on the topics of theory and practice. Statistical software will not be used on the exam. Students have to sign up for the examination through the Neptun system. Repetition of examinations is according to the general regulations of the Study and the Examination Requirements of the University.
  • At the examination a maximum of 20 points can be achieved. Grades of the examination are determined as follows:
    • 0–9 points:  failed (1)
    • 10–12 points: passed (2)
    • 13–15 points: accepted (3)
    • 16–17 points: good (4)
    • 18–20 points: very good (5)

Obligatory textbooks:
Students can download course material (handouts, lecture notes, R scripts) from http://www2.szote.u-szeged.hu/dmi/eng or from the Coospace. Making notes at the lectures will help in preparing for the exam.

Suggested textbooks: