Optimized questionnaire item selection for tracking the progression of motor symptoms in Parkinson's disease

TL;DR

This study compares methods for selecting minimal item sets from the MDS-UPDRS questionnaire to efficiently track Parkinson's disease progression, improving accuracy while reducing response burden.

Contribution

It introduces and evaluates three optimized item selection methods, demonstrating their effectiveness over random selection in reducing uncertainty.

Findings

Fisher information ranking reduces standard deviation by 14%.

Coordinate descent method achieves a 26% reduction.

Adaptive selection yields a 34% reduction in estimate uncertainty.

Abstract

Long questionnaires increase the response burden for patients and healthcare workers. In the treatment of Parkinson's disease, the MDS-UPDRS questionnaire to track disease progression may be underutilized due to time requirements. While reduced item sets have been studied using Fisher information from Item Response Theory (IRT) models, optimal selection methods remain unclear. We compared three methods for selecting an optimal subset of items, with the aim of minimizing the uncertainty in the estimates of the disease severity: Ranking by the Fisher information, coordinate descent local search to directly minimize estimate uncertainty, and adaptive selection. Whereas item ranking based on the expected Fisher information outperformed random choice of items, we saw further gains with the coordinate descent algorithm that directly minimizes the uncertainty of the disease severity estimate. An adaptive algorithm gave an additional slight gain compared to the coordinate descent method. However, the performance of the adaptive method is a best-case limit as we assume that we find the optimal set for the true latent trait scores. For a 5-item subset, the ranked Fisher information method reduced the expected standard deviation by 14 percent compared to random item selection. The corresponding reductions for coordinate descent and adaptive selection were 26 percent and 34 percent respectively. More sophisticated selection methods substantially improved estimate accuracy for small item sets, with diminishing returns for larger subsets. Because item parameters are retained from the full test, reduced item sets measure the same latent construct as the original test. The choice of method entails a trade-off between methodological complexity and precision.