Colophon -- Preface -- 1. Measures of Occurrence -- 1.1. Introduction -- 1.2. Prevalence -- 1.3. Incidence -- 1.4. Practical issues -- 1.4.1. Denominator issues -- 1.4.2. Numerator issues -- 1.5. Practical examples -- References -- 2. Measures of Association -- 2.1. Relative risk -- 2.2. Risk difference -- 2.3. Other measures of attributable risk -- 2.3.1. Attributable risk percent -- 2.3.2. Population attributable risk -- 2.3.3. Population attributable risk percent -- 2.3.4. Odds ratio -- 2.4. Practical examples -- 2.4.1. Example 1 -- 2.4.2. Example 2 -- 2.4.3. Example 3 -- 2.4.4. Example 4 -- References -- 3. Controlling for Confounding -- 3.1. What is confounding in epidemiology? -- 3.2. Controlling for confounding factors -- 3.2.1. Study design -- 3.2.2. Data analysing -- 3.3. How to control for confounding factors -- 3.3.1. Stratified analysis -- 3.3.2. The multivariate analysis -- 3.4. Practical examples -- 3.4.1. Example 1 -- 3.4.2. Example 2 -- References -- 4. Cross-Sectional Studies -- 4.1. Introduction -- 4.2. Performing a cross-sectional study -- 4.3. A practical example -- References -- 5. Cohort Studies -- 5.1. What is a cohort study? -- 5.2. Why do we need a cohort study? -- 5.3. The eligibility criteria -- 5.4. The structure of a cohort study -- 5.5. Censoring -- 5.6. The statistical analysis in a cohort study -- 5.7. Practical examples -- 5.7.1. Example 1 -- 5.7.2. Example 2 -- 5.7.3. Example 3 -- References -- 6. Experimental Studies -- 6.1. What is a sample experimental study? -- 6.2. Why do we need an experimental study? -- 6.3. The eligibility criteria -- 6.4. The randomisation process -- 6.5. The blinding -- 6.6. The structure of an experimental study -- 6.7. The statistical analysis in an experimental study -- 6.8. Practical examples -- 6.8.1. Example 1 -- 6.8.2. Example 2 -- 6.8.3. Example 3 -- References.

7. Temporal Trend Analysis -- 7.1. Introduction -- 7.2. Basic principles of temporal trend analysis -- 7.3. Practical examples -- 7.3.1. Example 1 -- 7.3.2. Example 2 -- References -- 8. The Surveillance of Sexually Transmitted Infections: the Theory and the Practice -- 8.1. Introduction -- 8.2. Surveillance of sexually transmitted infections in the third millennium -- 8.3. Attributes of a STI surveillance system -- 8.3.1. Simplicity -- 8.3.2. Acceptability -- 8.3.3. Sensitivity -- 8.3.4. Representativeness -- 8.3.5. Timeliness -- 8.4. Universal versus sentinel surveillance systems -- 8.5. How to perform STI surveillance -- 8.5.1. Which infections should be included in surveillance? -- 8.5.2. The example of Italy's STI surveillance -- 8.5.3. Case definition -- 8.5.4. Sensitivity and specificity of the case definition -- 8.5.6. Data collection forms -- 8.5.7. Geographic distribution and representativeness -- 8.5.8. Stability of the sentinel population -- 8.5.9. Population denominators -- 8.5.10. Data dissemination -- 8.6. Data management and analysis -- 8.7. Practical exercises for analysing a dataset of STIs -- 8.7.1. The dataset -- 8.7.2. Characteristics of cases by reporting centre -- 8.7.3. Demographic and behavioural characteristics of patients -- References -- 9. Systematic Reviews and Meta-Analysis of Clinical Trials -- 9.1. What is a systematic review? What is a meta-analysis? -- 9.2. Why do we need systematic reviews and meta-analyses? -- 9.2.1. The "Streptokinase case" -- 9.3. Practical steps of a meta-analysis -- 9.3.1. Production of an explicit and reproducible research protocol -- 9.3.2. Definition of criteria for the inclusion or exclusion of individual studies -- 9.3.3. Explicit and systematic bibliographic search -- 9.3.4. Assessment of the methodological quality of the studies -- 9.3.5. Data extraction from included studies.

9.3.6. Statistical combination of data and presentation of the results -- 9.3.7. Sensitivity analysis and interpretation of the findings -- 9.3.8. Publication bias -- 9.4. A practical example of a meta-analysis of RCTs -- 9.4.1. Introduction to the statistical analysis: fixed and random effects models -- 9.4.2. Addressing heterogeneity -- 9.4.3. Meta-regression -- 9.4.4. Publication bias -- 9.4.5. Other commands and options for the analysis -- References -- 10. Meta-Analysis of Observational Studies -- 10.1. Introduction -- 10.2. Practical example -- 10.2.1. Study justification and objectives -- 10.2.2. Brief description of the search strategy and data extraction -- 10.3. Worked examples -- 10.3.1. Example 1: Meta-analysis done by hand -- 10.3.2. Example 2: Meta-analysis using WINPEPI -- 10.3.3. Example 3: Meta-analysis using Stata basic commands -- References -- 11. Genetic Epidemiology -- 11.1. Key concepts of genetic epidemiology -- 11.1.1. Definition and goals -- 11.1.2. The Human Genome -- 11.1.3. Study design -- 11.2. A practical example: the "candidate gene approach" -- References -- 12. Analysis of Cost Data Using Bootstrap Technique -- 12.1. Introduction -- 12.2. Basic principles of the bootstrap method -- 12.3. Bootstrap standard normal confidence interval -- 12.4. Percentile method confidence interval -- 12.5. Bias corrected and accelerated (BCa) confidence interval -- 12.6. Application to example -- References -- 13. Sensitivity, Specificity, and ROC Curves -- 13.1. Study introduction -- 13.2. Sensitivity, specificity, and predictive value -- 13.3. Basic principles of ROC curves -- 13.3.1. The area under a ROC curve -- 13.3.2. Practical example -- 13.4. Use of ROC analysis for comparison -- References -- 14. Measures of Central Tendency and Dispersion -- 14.1. Introduction -- 14.2. Measures of central tendency -- 14.2.1. Mean.

14.2.2. Median -- 14.2.3. Mode -- 14.3. Measures of dispersion -- 14.3.1. Range -- 14.3.2. Percentiles, quartiles, interquartile range -- 14.3.3. Variance -- 14.3.4. Standard deviation -- 14.4. Practical exercise -- References -- 15. Sample Size Calculations -- 15.1. What is a sample size and why do we need a sample? -- 15.2. Steps of a sample size calculation -- 15.2.1. Practical examples -- References -- 16. Representation of Data -- 16.1. Introduction -- 16.2. Representation of qualitative variables -- 16.2.1. Tables -- 16.2.2. Bar chart -- 16.2.3. Pie chart -- 16.3. Representation of quantitative variables -- 16.3.1. Histogram -- 16.3.2. Scatter plot -- 16.3.3. Box plot -- References -- 17. Running Multiple Regression With Quantitative and Qualitative Variables With R -- 17.1. Introduction -- 17.2. The regression model with quantitative and qualitative variables -- 17.2.1. Testing of significance of parameters -- 17.2.2. The coding of dummy variables -- 17.3. Practical example: multiple regression with 2 qualitative variables -- 17.3.1. Installing and running R -- 17.3.2. Loading the dataset -- 17.3.3. Exploratory analysis -- 17.3.4. Correlation analysis -- 17.3.5. Model development -- References -- 18. Methods for Assessing Normality of Quantitative Variables -- 18.1. Introduction -- 18.2. Definition of normality -- 18.3. Parametric and nonparametric statistics -- 18.4. How to verify normality of data -- 18.5. Practical examples -- References -- 19. Quality of Life Evaluation -- 19.1. Quality of life in the general population -- 19.1.1. Quality of life measurement -- 19.1.2. The study -- 19.2. Quality of life in the clinical setting -- 19.2.1. Choice of QoL assessment tools -- 19.2.2. Study introduction -- 19.2.3. Practical example to calculate SF-36 scores -- 19.2.4. Correlation analysis between SF-36 scores and clinical findings -- References.

Appendix. Algorithm to create the SF-36 scales -- 20. Disability Adjusted Life Years (DALY) Summary Measure of Population Health -- 20.1. Introduction -- 20.2. Disability adjusted life year (DALY): the concept and its uses -- 20.3. Method for DALY estimation used in the serbian burden of disease study -- 20.3.1. Disease selection and staging -- 20.3.2. Social value choices -- 20.3.3. Population data -- 20.3.4. Mortality data -- 20.3.5. Disability data -- 20.3.6. DALY estimation -- 20.3.7. Years of life lost (YLL) -- 20.3.8. Years lost due to disability (YLD) -- 20.3.9. DALY Estimation -- 20.4. Practical example: calculation of DALY for colorectal cancer, Serbia, 2000 -- Acknowledgments -- References.