Syllabus

Course Description

Statistical Reasoning in Public Health II provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions. The course builds on the material in Statistical Reasoning in Public Health I , extending the statistical procedures discussed in that course to the multivariate realm, via multiple regression methods. New topics, such as methods for clinical diagnostic testing, and univariate, bivariate, and multivariate techniques for survival analysis will also be covered. These topics will be reinforced with many "real-life" examples drawn from recent biomedical literature. While there are some formulae and computational elements to the course, the emphasis is again on interpretation and concepts.

Course Objectives

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.

Readings

The required textbook for this course is as follows:

• Altman, D.G. (1991). Practical Statistics for Medical Research: Chapman and Hall.

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."

You may purchase any of these materials from Matthews Medical Book Center .

Course Topics

• 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

Course Format

The content of this course is divided into four separate modules. All the required course work can be accessed from the Course Modules 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.