Lesson 9 Objectives
- Use plots, tables, and summary statistics to describe variables and relationships between variables
- Identify which modeling strategy to use based on the type of data (continuous, categorical, time-to-event)
- Interpret results of statistical analyses
- Differentiate between odds ratios, risk ratios, and hazard ratios
Epidemiologic data can be analyzed using a variety of statistical methods. Here we outline the fundamentals according to the type of outcome measure. We can generally think of outcome data as one of three types: 1) continuous, 2) categorical, and 3) time-to-event. While it is true that time-to-event is continuous, we do not always observe the true time for each person, so special consideration needs to be given in that scenario. Once the type of outcome data is known, there are standard techniques one can use to provide descriptive statistics, look at bivariable associations, and use modeling to describe the association between multiple covariates and the outcome.
Example: a subset of data from the Framingham Heart Study
Our motivating example will be based on the SAS-provided dataset “Heart” which includes a small subset of data from participants in the Framingham Heart Study. This dataset contains over 5000 patients from the cohort study and provides data on their baseline age, sex, weight, smoking status, cholesterol, blood pressure, and coronary heart disease (CHD) development. Patients were contacted every 2 years for over 30 years.