Development of an R Package to Predict Item Responses Accounting for Differential Item Functioning

Music City Center 

Patient-reported outcome measures (PROMs) are multi-item scales that capture patients' appraisals of their quality of life. Differential item functioning (DIF) is a potential source of measurement bias that occurs when patients with the same health status interpret PROMs items differently due to characteristics such as demographics and comorbid conditions. DIF can be detected on multiple covariates using item-focused tree (IFT) models that combine item-specific logistic regression with recursive partitioning based on structural change tests. The DIFtree package in R fits IFT models but lacks tools to evaluate model performance. We developed an R package, IFTPredictor, to predict item responses, adjusted for DIF, using the IFT model. The package accepts a fitted IFT model, a dataset for prediction, and total item scores as inputs. It generates logistic regression equations, predicted probabilities and responses for each item, incorporating subgroup-specific covariates for DIF items. Predicted responses are used to evaluate the IFT model on accuracy, precision, and calibration. Our package will enhance the usability of the IFT model for DIF analysis on multiple covariates.

Model-based recursive partitioning

Measurement invariance

Item responses

Machine-learning

Logistic regression 

Section on Statistics in Epidemiology