Wednesdays, 9:00 am - noon; 1:00 pm - 4:00 pm (STATA lab)
Nancy Hills, Ph.D.
Wayne Enanoria, Ph.D.
Teaching assistant: Amy Penn, M.S.
Lectures, seminars, independent study, assigned projects, and practice with statistical software (STATA)
- 2-3 hours of lecture per week (Quiz and lecture, Wednesday mornings)
- 3 hours of statistics lab using STATA (Wednesday afternoons)
By the end of the course the student should be able to:
- Define the measures of disease frequency (prevalence and incidence) and give examples
- Define variable types (e.g. categorical, continuous), distributions, and measures of central tendency and variance using tables and graphs
- Demonstrate an understanding of standardization (direct and indirect) and give examples of comparative disease rates of two populations
- Know the major measures of risk such as rate ratio, risk ratio, odds ratio, and understand confidence intervals
- Describe measures of public health impact such as population attributable risk
- Be able to conduct descriptive and unadjusted bivariate analyses
- Become familiar with survival analysis (Kaplan-Meier) and measures associated with this analytic technique
- Understand principles of regression methods (simple linear regression; logistic regression)
- Be able to navigate the STATA statistical package and conduct univariate and bivariate data analyses
- Variables - nominal, ordinal, categorical, continuous. Descriptive statistics: distributions, means, standard deviation, standard error, and variance; predictors, outcomes, exposure variables and their measurement
- Introduction to data and data collection
- Measurement of disease in populations: prevalence, incidence, rates
- Introduction to STATA. Descriptive statistics (means, medians, frequencies)
- Measurement of disease association; the 2x2 table; prevalence ratio; odds ratio, attributable risk; confidence intervals
- STATA examples of disease measurements and rates; standardization; review a journal article that illustrates learning concepts
- Confounding variables that introduce error; chi square and Mantel-Haenszel tests; stratification
- Non-parametric statistical tests statistics (t test; Mann-Whitney test Wilcoxon) and indications for their use
- STATA: Bivariate analysis of class data set; review a journal article with longitudinal data.
- Introduction to multiviariate methods (linear and logistic regression)
- STATA exercises in multiple regression analysis
- Survival analysis