This is the syllabus used in conjunction with educational content offered by JHSPH. As a result, some of the information and/or materials listed here may not be relevant to or available for an OCW user's self-directed study.
Course DescriptionStatistical Reasoning in Public Health II provides a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. It develops ability to read the scientific literature to critically evaluate study designs and methods of data analysis. It introduces basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals. Topics include comparisons of means and proportions; the normal distribution; regression and correlation; confounding; concepts of study design, including randomization, sample size, and power considerations; logistic regression; and an overview of some methods in survival analysis. The course draws examples of the use and abuse of statistical methods from the current biomedical literature.
After completion of this course, you will be able to do the following:
- Recognize different study designs and understand the pros and cons of each.
- Learn methods for randomly assigning subjects to two groups.
- Understand the concepts of confounding and statistical interaction; know how to recognize each.
- Explain the relationship between power and sample size; use Stata to perform sample size calculations.
- Create a scatterplot to visually assess the nature of an association between two continuous variables.
- Interpret the calculated values of the correlation coefficient and the coefficient of determination, and understand the relationship between these two measures of association.
- Perform a simple linear regression using Stata and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and a continuous predictor variable and for predicting values of the outcome variable.
- Understand why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables.
- Perform a multiple linear regression using Stata and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and multiple continuous and categorical predictor variables and for predicting values of the outcome variable.
- Perform a multiple logistic regression using Stata and use the results to assess the magnitude and significance of the relationship between a dichotomous outcome variable and multiple continuous and categorical predictor variables.
- Interpret the results from a proportional hazards regression model.
There is no required textbook for this course.
Students are also required to have access to "Small Stata," a version of Stata that is less powerful (in terms of the amount of data it can store and process, not in terms of functionality) than regular "Intercooled Stata," and costs significantly less. Small Stata carries a one-year users license. However, if you intend to further your study of statistics beyond this course, you may wish to purchase a copy of "Intercooled Stata 8."
- Issues in study design
- Correlation and simple linear regression
- Multiple linear regression
- Multiple logistic regression
- Introduction to censored survival data
- The Kaplan-Meier method for constructing survival curves
- Multivaritate survival analyis vis Cox proportional hazards regression
The content of this course is divided into four separate modules. All the required course work can be accessed from the Lecture Materials page. The lecture sections are presented sequentially and should be completed in that order. Each of these sections combines audio presentation and slides - just like attending lectures in class. You may return to any previous section at any point and review its contents at your convenience. In each lecture section, you will find a listing of the section objectives, links to the lecture materials, a listing of reading assignments, and links to Web resources.