Share Description
In this course, you will :
- covers the logistics of planning and carrying out analysis on the previously prepared analytic data set.
- demonstrates how to conduct the analysis and interpret the final model in light of your original hypothesis She teaches best practises for code naming and arrangement, stepwise selection modelling, odd and prevalence ratios, and relative risk along the way.
- be able to create excellent healthcare studies that take advantage of everything big data has to offer
Syllabus :
1. Logistics of Creating the Analytic Dataset
- Code arrangement
- Dataset transformation approach
- Applying qualification criteria
- Finalizing the analytic dataset
2. Conducting the Analysis
- Descriptive vs. regression analysis
- Considerations for categorical outcomes
- Structure of descriptive table: Table 1
- Descriptive table example
- Stepwise modeling to answer a hypothesis
- Establishing the working model
- Documenting model metadata
- Selective stepwise modeling: Breaking the working model
- Considering model fit
- Selecting and interpreting the final model
3. Interpreting the Final Model
- Regression interpretation review
- Interpreting regression parameter estimates
- The 2x2 table revisited: Relative risk
- Final model presentation
- Talking about your analysis: Introduction and methods
- Talking about your analysis: Results and discussion
