Course content

• Statistical methods for understanding and interpreting biological and public health data.
• Descriptive Statistics, Screening tests, ROC curves, prevalence and incidence of disease.
• Application of regression to biological and health data sets: graphical data summary,statistical comparison of interventions by hypothesis tests and confidence intervals, correlation,and regression. Case studies.
• Design and Analysis techniques for Epidemiologic Studies: Study designs, confounding and standardization, Meta analysis, Analysis of variance and covariance. crossover designs, clustered data, intraclass correlation, longitudinal analysis, missing data, case studies
• Categorical health data: Contingency tables, Goodness of fit tests, Tests for binomial proportions, estimation of power and sample size, logistic regression, case studies
• Person time health data: Inference for incidence rate data, power and sample size considerations, testing for trend, survival analysis, proportional hazard model, case studies
• Case studies of health big data: techniques to summarize and display big data, descriptive and inferential methodologies
• Clinical trials: statistical design and analysis aspects of clinical trials

References

• Rosner, B. Fundamentals of biostatistics. Belmont, CA: Thomson-Brooks/Cole, 2006.
• Wayne W. Daniel, Chad L. Cross, Biostatistics: A Foundation for Analysis in the Health Sciences (11thed.), John Wiley, NewYork, 2018.
• S.C. Chow and J.P. Liu, Design and Analysis of Clinical Trials - Concepts & Methodologies (3rd Edition),John Wiley & Sons, NY, 2013.
• Kutner, M., Nachtsheim, C., Neter, J. and Li, W. Applied Linear Statistical Models, 5th Edition,McGraw-Hill Companies, Boston, 2005.