Medical Statistics

General Foundation

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

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

Medical Statistics, Biostatistics

11-10-2016