Designing Big Data Healthcare Studies, Part One

Course details

Even if you have a strong grasp of statistics and informatics, you also need to understand epidemiology and basic study design to perform accurate, rigorous analysis of healthcare data. This course will help you design research studies around hypotheses, and fill the knowledge gap that many of today's analysts face when entering the healthcare field. Instructor Monika Wahi defines basic terms and concepts in epidemiology, and reviews the different study design approaches: descriptive, analytic, cross-sectional, and case control. She dives into detail on cross-sectional and case-control studies, and shows how to plan an analytic data set: figuring out the necessary native variables and operationalizing them in a data dictionary. Last, she reviews the lessons learned from the course and prepares you for part two of the training series, which tackles the descriptive and regression analysis for the data set you have designed.

Learning objectives
Define exposure and outcome.
Explore the elements of populations versus samples in a data healthcare study.
Recognize the fundamentals of utilizing the scientific method in epidemiology.
Explore the essential elements of part one of the Bradford Hill criteria.
Recognize the fundamentals of an observational study versus an experiment.
Define a case-control study design.
Identify the important parts of levels of evidence.
Recall the meaning of a prevalence rate.
Plan the best ways to establish a working hypothesis.

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Using the exercise files

1. Epidemiology and Causal Inference

04 - Definition of epidemiology
05 - Terms about data
06 - Definition of exposure and outcome
07 - Populations vs. samples
08 - Scientific method in epidemiology
09 - Bradford Hill criteria - Part one
10 - Bradford Hill criteria - Part two

2. Study Designs

11 - Overview of human research
12 - Observational study vs. experiment
13 - Descriptive vs. analytic study designs
14 - Cross-sectional study design
15 - Case-control study design
16 - Levels of evidence

3. Measures of Association

17 - Introduction to the 2x2 table
18 - Prevalence ratio
19 - Odds ratio in a cross-sectional study
20 - Odds Ratio in a case-control study
21 - Conclusion about the 2x2 table

4. Planning a Study

22 - Definition of confounders
23 - Using a web of causation to identify confounders
24 - Tools for reviewing the scientific literature
25 - Reviewing existing scientific literature
26 - Establishing a working hypothesis
27 - Choosing a dataset
28 - Final dataset considerations

5. Planning the Analytic Dataset

29 - Definition of data curation
30 - Requirements for a cross-sectional or case-control analytic dataset
31 - Setting up a data dictionary
32 - Operationalizing the subpopulation
33 - Operationalizing the exposure, outcome, and confounders
34 - Documenting transformed variables in the data dictionary

Conclusion

35 - Review of the course
36 - Preparation for part two