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In this course, you will :
- Assist you in designing research studies based on hypotheses, as well as filling the knowledge gap that many analysts face when entering the healthcare field.
- defines basic epidemiological terms and concepts, and discusses the various study design approaches: descriptive, analytic, cross-sectional, and case control.
- explores cross-sectional and case-control studies in depth, and demonstrates how to plan an analytic data set, including determining the necessary native variables and operationalizing them in a data dictionary.
- reviews the course lessons and prepares you for part two of the training series, which covers descriptive and regression analysis for the data set you designed.
Syllabus :
1. Epidemiology and Causal Inference
- Definition of epidemiology
- Terms about data
- Definition of exposure and outcome
- Populations vs. samples
- Scientific method in epidemiology
- Bradford Hill criteria: Part one
- Bradford Hill criteria: Part two
2. Study Designs
- Overview of human research
- Observational study vs. experiment
- Descriptive vs. analytic study designs
- Cross-sectional study design
- Case-control study design
- Levels of evidence
3. Measures of Association
- Introduction to the 2x2 table
- Prevalence ratio
- Odds ratio in a cross-sectional study
- Odds Ratio in a case-control study
- Conclusion about the 2x2 table
4. Planning a Study
- Definition of confounders
- Using a web of causation to identify confounders
- Tools for reviewing the scientific literature
- Reviewing existing scientific literature
- Establishing a working hypothesis
- Choosing a dataset
- Final dataset considerations
5. Planning the Analytic Dataset
- Definition of data curation
- Requirements for a cross-sectional or case-control analytic dataset
- Setting up a data dictionary
- Operationalizing the subpopulation
- Operationalizing the exposure, outcome, and confounders
- Documenting transformed variables in the data dictionary
