Course Information
TitleΙΑΤΡΙΚΗ ΣΤΑΤΙΣΤΙΚΗ / Medical Statistics
CodeΙΑ0278
FacultyHealth Sciences
SchoolMedicine
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorMalamatenia Arvanitidou-Vagiona
CommonNo
StatusActive
Course ID200000276

Programme of Study: UPS of School of Medicine (2014-2015)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
CoreCompulsory112.5

Programme of Study: UPS of School of Medicine (2013-2014)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
CoreCompulsory112.5

Programme of Study: UPS of School of Medicine (2011-2012)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
KORMOSCompulsory112.5

Class Information
Academic Year2017 – 2018
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600106153
SectionInstructors
1. ΕΡΓΑΣΤΗΡΙΟ ΥΓΙΕΙΝΗΣMalamatenia Arvanitidou-Vagiona, Theodoros Dardavesis, Ilias Tyrodimos
Type of the Course
  • Background
  • General Knowledge
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Learning Outcomes
For each lecture the objectives are described below: 1 Description, presentation and summary data • Identification of qualitative and quantitative variables and different types of data • Presentation and summary data in an appropriate way • Calculation and interpretation of graphs • Understand the characteristics of the normal distribution of their difference from the asymmetric distributions 2 Measures of central location and dispersion • Calculation of measures of central location and dispersion • Understand the use of appropriate measures of central location and dispersion for summarizing data with normal and skewed distribution • Description and interpretation of results of measures of central location and dispersion 3 Populations and samples • Understand the differences in measurements between the population and sample • Different types of selecting a random sample from a population • Understand the types of random sampling and their application • Assessment of the accuracy and repeatability of sampling • Identify the disadvantages of non-random samples 4 Normal distribution • Identification of the properties of a normal distribution • Transformation of a normal distribution to a standard normal distribution • Calculation of various probabilities based on standard distribution • Find critical values from the table of normal distribution • Understand the properties of the sampling distribution • Estimate the difference between the standard deviation and standard error of the mean 5 Hypothesis testing • Formulate the null and alternative hypothesis • Understand the concept of P-value, the level of significance and the corresponding critical value • Testing and assessing the significant difference • Separation between one-sided and two-sided tests • Understand type I and II errors and when the power of a study increases 6 Parametric tests of one sample • Understand application Z and t test • Formulate the null and alternative hypothesis in the case of a control sample • Calculation of the Z and t test • Read and find critical values of the t distribution table • Interpretation of the results of the test • Calculation of confidence intervals 7 Parametric tests of two samples • Understand the difference between independent and dependent samples • Formulate the null and alternative hypothesis in hypothesis testing between two independent or dependent samples • Calculation of the t test for two independent or dependent samples • Interpretation of the results of the tests • Calculation of confidence intervals 8 Analysis of variance • Formulate the null and alternative hypothesis in hypothesis testing between three or more independent samples • Understand the problem of multiple comparisons and how to correct it • Understand the calculation of the F-ratio • Read and find critical values from F-distribution table • Configuring the analysis of variance table • Procedures for correcting multiple comparisons 9 Correlation between continuous variables • Understand the importance of the correlation coefficient • Assessment of the application of different correlation coefficients depending on the types of data • Calculation of the Pearson correlation coefficient • Calculation of non-parametric Pearson correlation coefficient 10 Association of qualitative variables • Understand the application of the chi-square criterion • Calculation of the goodness of fit test with equal and proportionate number of expected frequencies • Calculation of the chi-square test for independency and interpretation of results • Configuring the contingency table • Calculation of the chi-square test for fourfold (2x2) • Calculation of the Z test for comparing two proportions in a fourfold table • Implementation of the Mc Nemar test for dependent samples 11 Non-parametric tests • Understand the utility of non-parametric tests • Recognition of the parametric test applied to the relevant comparison • Application of the Mann-Whitney test to compare two independent samples • Implementation of the Wilcoxon test for comparing two dependent samples • Implementation of the Kruskal-Wallis test for comparison of three independent samples and above • Assessment of the advantages and disadvantages of non-parametric tests 12 Diagnostic testing • Calculation and interpretation of sensitivity, specificity, predictive value of positive and negative results • Understand the restrictions on the use of positive and negative predictive values • Interpretation of ROC curve 13 Survival analysis • Decision on data in which the survival analysis is applied • Understand the concept of censored data • Calculation and interpretation of life tables • Interpretation of Kaplan-Meier curves • A comparison of two survival curves by Log rank test • Assessment of the assumptions that must be met to implement the Log-rank test
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
Course Content (Syllabus)
Lecture 1 Description, presentation and summary data 2 Measures of central location and dispersion 3 Populations and samples 4 Normal distribution 5 Hypothesis testing 6 Parametric tests of one sample 7 Parametric tests of two samples 8 Analysis of variance 9 Association between two continuous variables 10 Association of qualitative variables 11 Non parametric tests 12 Diagnostic testing 13 Survival analysis
Keywords
Medical Statistics, Biostatistics
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures391.5
Tutorial261
Total652.5
Student Assessment
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
Bibliography
Course Bibliography (Eudoxus)
Ιατρική Στατιστική: Βασικές αρχές. Αρβανιτίδου-Βαγιωνά Μ,Χάιδιτς Α-Μ. Εκδόσεις University Studio Press, Θεσσαλονίκη 2013.
Last Update
11-10-2016