Goals of the Analysis and Nature of Data
Output is Continuous
- Example: How do explanatory variables such as lifestyle or chronic diagnoses affect LE / DALYs
- Traditional regression models, including linear regression (OLS), ridge & lasso
- Coefficient estimates quantify the association between changes in input and changes in outcome.
Output is Categorical or Binary
- Outcome is categorical (e.g., disease/no disease)
- Can use logistic regression (more common for explaining)
- Or classification (more common for predicting)
Systemic Modelling / Simulation
- For complex systems modelled by multiple equations
- Typically more predictive
- Have a series of equations to fit to data, for example SIR model
- May wish to change parameters for sensitivity or explore how changes to inputs affects predicted outcome
Time-Series
- Data has a temporal or seasonal aspect (influenza?)
- Models like ARIMA can be used to model autocorrelation & trends