Record:   Prev Next
Author La Torre, Giuseppe
Title Applied Epidemiology and Biostatistics : (Includes downloadable software)
Imprint Torino : SEEd Srl, 2010
©2010
book jacket
Edition 1st ed
Descript 1 online resource (367 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Note Intro -- 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
This book provides not only the theory of biostatistics, but also the opportunity of applying it in practice. In fact, each chapter presents one or more specific examples on how to perform an epidemiological or statistical data analysis and includes download access to the software and databases, giving the reader the possibility of replicating the analyses described
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: La Torre, Giuseppe Applied Epidemiology and Biostatistics : (Includes downloadable software) Torino : SEEd Srl,c2010 9788889688496
Subject Microsoft Visual BASIC.;BASIC (Computer program language);Microsoft .NET
Electronic books
Record:   Prev Next