Descriptive Healthcare Analytics in R

سرفصل های دوره

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.

01 - Introduction

01 - Welcome

02 - 1. What Is the BRFSS

01 - U.S. risk factors

02 - Introduction to the BRFSS

03 - More on the BRFSS

04 - What is a descriptive BRFSS analysis

05 - Cross-sectional analysis in the BRFSS

06 - Ethical use of BRFSS data

07 - BRFSS resources

08 - Choosing R for a BRFSS analysis Some considerations

09 - Choosing R for a BRFSS analysis More considerations

10 - Installing R

11 - Navigating in R

12 - Installing the foreign package

13 - Installing necessary packages

03 - 2. Designing Your Metadata

01 - Uses of a data dictionary

02 - How to set up a data dictionary

03 - Adding to the data dictionary

04 - Understanding confounders

05 - Making a web of causation

06 - Designing confounders Age and smoking

07 - Designing confounders Other demographics

08 - Designing confounders Other variables used in analysis

04 - 3. Reading in Data and Applying Exclusions

01 - Reading in BRFSS XPT data

02 - Naming conventions

03 - Keeping native variables

04 - Applying the first exclusion

05 - Applying the rest of the exclusions

06 - Operations in code

07 - Making a data reduction diagram

08 - Generating exposure

09 - Generating outcome variables

05 - 4. Preparing for Descriptive Analysis

01 - Generating the age variables

02 - Generating the smoking variables

03 - Finalizing the analytic data set

04 - What is Table 1

05 - Reviewing categorical variable distribution

06 - Reviewing continuous variable distribution

06 - 5. Conducting Descriptive Analysis

01 - Preparing categorical Table 1 shell

02 - Preparing continuous Table 1 shell

03 - Adding overall frequencies to categorical Table 1

04 - Making a frequency macro

05 - Adding overall frequencies to continuous Table 1

06 - Completing categorical Table 1

07 - Completing continuous Table 1

07 - 6. Descriptive Analysis Weights and Tests

01 - Three truths about using weights

02 - Conducting a descriptive weighted analysis

03 - Why conduct bivariate tests

04 - Adding categorical bivariate tests to Table 1

05 - Introduction to ANOVA and linear regression code

06 - Adding continuous bivariate tests to Table 1

08 - Conclusion

01 - Review of the metadata

02 - Uses of metadata

03 - Review of the process

04 - Next steps in the BRFSS analysis

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تولید کننده: LinkedIn Learning (Lynda)