Descriptive Healthcare Analytics in R

Course details

Analyze behavior and risk using R, the open-source statistical computing software. R provides an environment and a language you can use to analyze data, including the publicly available Behavioral Risk Factor Surveillance Survey (BRFSS) dataset. This course teaches core healthcare data science skills, including epidemiology, as well as how to perform a cross-sectional analysis, set up a data dictionary, develop metadata, determine confounders, apply exclusions, create diagrams, generate continuous and categorical outcome variables, and more. Join biotech expert and epidemiologist Monika Wahi as she first discusses design and ethical considerations, and then takes you through the steps of conducting a descriptive analysis.

This detailed, practical course is designed to help those in the field of public health, medicine, and data science to edit, analyze, and interpret data. Learn how to code new variables, use the forward-stepwise modeling process, and document your decisions. Find out how to visualize results by generating charts and graphics, and how to add tables and figures to your documentation. This course helps equip you to independently design, develop, and execute a full BRFSS analysis, and even publish your results in scientific publications or journals.

Learning objectives
Reviewing survey data and documentation
Conducting a BRFSS analysis
Understanding naming conventions
Editing variables
Reviewing distributions
Generating an analytic dataset
Developing descriptive statistics to answer prespecified hypotheses
Preparing publication-worthy tables and plots

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Introduction to the course
04 - How to use the exercise files

1. What Is the BRFSS

05 - US risk factors
06 - Introduction to the BRFSS
07 - More on the BRFSS
08 - What is a descriptive BRFSS analysis
09 - Cross-sectional analysis in the BRFSS
10 - Ethical use of BRFSS data
11 - BRFSS resources
12 - Choosing R for a BRFSS analysis - Some considerations
13 - Choosing R for a BRFSS analysis - More considerations
14 - Installing R
15 - Navigating in R
16 - Installing the foreign package
17 - Installing necessary packages

2. Designing Your Metadata

18 - Uses of a data dictionary
19 - How to set up a data dictionary
20 - Adding to the data dictionary
21 - Understanding confounders
22 - Making a web of causation
23 - Designing confounders - Age and smoking
24 - Designing confounders - Other demographics
25 - Designing confounders - Other variables used in analysis

3. Reading in Data and Applying Exclusions

26 - Reading in BRFSS XPT data
27 - Naming conventions
28 - Keeping native variables
29 - Applying the first exclusion
30 - Applying the rest of the exclusions
31 - Operations in code
32 - Making a data reduction diagram
33 - Generating exposure
34 - Generating outcome variables

4. Preparing for Descriptive Analysis

35 - Generating the age variables
36 - Generating the smoking variables
37 - Finalizing the analytic data set
38 - What is Table 1
39 - Reviewing categorical variable distribution
40 - Reviewing continuous variable distribution

5. Conducting Descriptive Analysis

41 - Preparing categorical Table 1 shell
42 - Preparing continuous Table 1 shell
43 - Adding overall frequencies to categorical Table 1
44 - Making a frequency macro
45 - Adding overall frequencies to continuous Table 1
46 - Completing categorical Table 1
47 - Completing continuous Table 1

6. Descriptive Analysis - Weights and Tests

48 - Three truths about using weights
49 - Conducting a descriptive weighted analysis
50 - Why conduct bivariate tests
51 - Adding categorical bivariate tests to Table 1
52 - Introduction to ANOVA and linear regression code
53 - Adding continuous bivariate tests to Table 1

Conclusion

54 - Review of the metadata
55 - Uses of metadata
56 - Review of the process
57 - Next steps in the BRFSS analysis

Descriptive Healthcare Analytics in R

Authors

Monika Wahi