Interpretation of regression models

While visualizations like Kaplan-Meier plots, cumulative incidence curves, or histograms provide useful overviews, they offer limited ability to compare groups or adjust for confounding. Regression models complement these illustrations by enabling deeper, more precise analysis of the underlying data.

Hence, often regression models tailored to the research question and research field are used to get more customised and detailed results. When choosing a model, the following should be considered:

• Fit to describe the type of data

• Fit to the research question that is aimed to be answered

• Assumption the model makes

• Interpretation within reach for targeted audience

We will in the following entirely focus on assumptions and model interpretation. Model interpretation is critical step to translate statistical outputs into meaningful clinical insights. It involves understanding parameters of statistical models

• coefficients, 

• confidence intervals,

•  p-values,

most importantly the relation between variables and outcomes. 

For example, in a logistic regression model used to predict disease presence, the odds ratios help quantify the strength of association between risk factors (e.g., smoking) and health outcomes (e.g., lung cancer). Clear interpretation ensures that findings are not only statistically significant but also clinically relevant, enabling practitioners to make evidence-based decisions and communicate results effectively to patients or stakeholders.