Backward elimination is particularly useful in epidemiological research for several reasons:
Model Simplification: It simplifies the model by eliminating non-significant variables, making it easier to interpret. Improved Accuracy: Reducing unnecessary variables can improve the predictive accuracy of the model. Statistical Efficiency: Including only significant variables increases the statistical efficiency of the model.
